What is Retrieval Augmented Generation (RAG)? - Examples, Use Cases, No-Code RAG Tools

Retrieval Augmented Generation (RAG) fundamentally transforms how Large Language Models (LLMs) interact with information.

At its core, RAG enhances LLMs by providing them with real-time access to external, up-to-date information. This allows LLMs to generate responses that more precise, relevant, and verifiable.

It moves beyond the inherent limitations of their first training data. It effectively combines the strengths of retrieval-based models that excel at finding information. It also integrates generative models that are adept at creating coherent text. This combination acts much like a digital research assistant who synthesizes knowledge to formulate intelligent answers.

This innovative approach addresses critical challenges faced by standalone LLMs. RAG enables a new era of more reliable and contextually aware AI applications.

Key Takeaways:

RAG empowers LLMs to access and use current, external data, significantly reducing “hallucinations” and improving factual accuracy.
It involves two primary phases. The first is an offline “Indexing” pipeline to prepare data. The second is a real-time “Retrieval and Generation” chain to answer queries.

RAG offers a cost-effective and adaptable solution for deploying AI, allowing models to stay updated without expensive retraining.

What is Retrieval Augmented Generation (RAG) in AI?

In AI, RAG is a framework that integrates an information retrieval system with a generative language model. This combination improves the factual accuracy and relevance of AI-generated text by grounding it in verifiable external data.

What is the purpose of a Retrieval Augmented Generation (RAG)?

The primary purpose of RAG is to overcome the inherent limitations of LLMs. These limitations include their tendency to “hallucinate” (generate incorrect information). They also have a static, often outdated, knowledge base. By grounding responses in verifiable, real-time external data, RAG aims to produce more precise, relevant, and trustworthy outputs.

Here’s a good explanation by IBM:

I have explained these LLM limitations and how RAG solves them in the next section.

What is RAG with example?

A practical example of RAG is a patient chatbot on a hospital website. Instead of giving a generic answer about knee surgery preparation, a RAG-powered chatbot retrieves specific information. It uses the hospital’s internal documents or patient guides to give a precise and tailored response.

2 limitations of LLM that Retrieval Augmented Generation (RAG) solves

Large Language Models, despite their impressive capabilities, face significant challenges that limit their reliability and applicability in many real-world scenarios. RAG emerged as a direct response to these 2 fundamental constraints:

Hallucinations: The AI’s “Imagination Problem”

One of the most critical limitations of LLMs is their tendency to “hallucinate.”

LLM hallucination refers to instances where the models generate plausible-sounding but factually incorrect, illogical, or unverifiable information. These fabrications can undermine trust and make LLMs unsuitable for applications where factual accuracy is paramount.

“Intrinsic hallucinations” contradict known facts while “Extrinsic hallucinations” lack any reliable source to support them.

The root causes of LLM hallucinations often include:

Incomplete training data
Ambiguous user prompts
AI model’s inability to access real-time and verifiable information.

How Retrieval Augmented Generation (RAG) solves LLM hallucinations?

Retrieval Augmented Generation (RAG) directly combats the LLM hallucination problem. It ensures that an LLM’s responses are firmly grounded in actual, verifiable content from a trusted database.

According to this AI research paper, RAG reduces hallucinations by 35%.

RAG provides specific external data which drastically reduces the instances of fabricated information. It transforms the LLM into an “open-book test-taker” rather than a guessing machine.

Limitations of RAG for fixing LLM hallucination problem

Nonetheless, the introduction of external context, while generally improving performance, can sometimes lead to an unexpected outcome.

Providing extra context can paradoxically boost an LLM’s confidence, especially when the context is insufficient or contradictory. This leads to a greater likelihood of incorrect answers instead of admitting uncertainty.

For example, one model’s rate of incorrect answers surged from 10.2% without context to 66.1% when given insufficient context. This highlights that the quality and sufficiency of the retrieved information are critical.

Simply adding any context is not enough.

The system must make sure that retrieved information is relevant, comprehensive, and correct. This shifts the focus from mere retrieval to intelligent context evaluation and validation within RAG system design.

Outdated knowledge and finite context

Traditional LLMs are trained on large datasets. But this process is static, meaning their knowledge can quickly become outdated, making them unsuitable for tasks requiring current information.

Additionally, LLMs have a finite context length. This limits their ability to process extensive data or detailed instructions in a single interaction.

How Retrieval Augmented Generation (RAG) solves LLM’s finite context and outdated data problem?

Retrieval Augmented Generation (RAG) directly addresses the issue of data obsolescence by decoupling knowledge from the LLM’s internal parameters. RAG eliminates the need for expensive and time-consuming retraining of the entire model whenever new information emerges.

RAG enables rapid knowledge updates within a retrieval index, often in minutes. This is crucial for organizations with dynamic data, like global insurers updating claims manuals weekly. An efficient retrieval system ensures answers stay current without the need for resource-heavy LLM retraining.

This ability to update the knowledge base without altering core LLM parameters offers a significant advantage. This is particularly for businesses in fast-paced sectors like finance or law. It enhances organizational agility. It allows enterprises to keep precise information with minimal overhead. This leads to better decision-making, customer service, and compliance in data-driven industries.

How Retrieval Augmented Generation (RAG) works: architecture and key components

Retrieval Augmented Generation operates through a sophisticated, two-phase process that integrates external knowledge into the LLM’s response generation. This architecture allows for grounded, factual, and up-to-date outputs.

What are the two main components of Retrieval Augmented Generation?

The two main components are the Indexing Pipeline and the Retrieval and Generation Chain. The Indexing Pipeline is an offline process for preparing and storing external data. The Retrieval and Generation Chain is the real-time process for fetching relevant data and generating responses based on a user query.

RAG Components and Processes diagram by appliedai.tools

Let’s understand these components in detail:

The indexing pipeline: Building your knowledge base

The first phase, the “Indexing Pipeline,” is typically an offline process.

Its purpose is to prepare and organize raw data from various sources. This process transforms data into a format that can be efficiently searched and retrieved later.

Here’s a good video to see a RAG pipeline in action:

Here are the steps involved:

Load:

The first step involves ingesting data from its original source. This is accomplished using “Document Loaders.” These are specialized tools designed to extract information from diverse formats like PDFs, web pages, databases, or proprietary documents.

For example, a WebBaseLoader can load HTML content from web URLs. It parses the content into text. This process often involves customizable parsing rules to focus on relevant sections like article content or titles. A single loaded document might span tens of thousands of characters.

Learn more: Build a Retrieval Augmented Generation (RAG) App: Part 1

Split:

Large documents are then systematically broken down into smaller, more manageable segments known as “chunks” or “splits”.

This step is crucial for two main reasons:

Very large chunks are difficult for retrieval systems to search effectively
LLMs have a finite context window, meaning they can only process a limited amount of text at a time.

Breaking documents into smaller, semantically coherent chunks ensures that relevant information can fit within the LLM’s input capacity.

A common technique for this is the RecursiveCharacterTextSplitter. It recursively divides documents using common separators like new lines until each chunk reaches an appropriate size. This is often with a specified chunk_overlap to keep context across boundaries. The process of chunking can be tricky; for example, splitting a code example across two chunks could lead to issues.

Store:

Once the documents are split, these chunks are converted into numerical representations called “embeddings”.

An “Embeddings model” performs this transformation, capturing the semantic meaning of each chunk as a high-dimensional vector. These embeddings are then stored and indexed in a “VectorStore,” often referred to as a vector database.

This specialized database is optimized for fast similarity searches. The system can quickly find and retrieve chunks whose embeddings are numerically “close” to a given query’s embedding. This indicates semantic relevance.

The retrieval and generation chain for answering queries

The second phase is the “Retrieval and Generation Chain.” It operates in real-time when a user submits a query to the RAG system.

Retrieve:

When a user inputs a question, that query is also transformed into its numerical embedding representation. A “Retriever” component then takes this query embedding and searches the VectorStore for the most semantically similar document chunks. This typically involves a vector search, identifying the pieces of external knowledge that are most relevant to the user’s question.

Generate:

The retrieved relevant chunks and the original user query are merged. They are then passed as “context” to a Large Language Model (LLM). This augmented prompt provides the LLM with specific, external information that it did not inherently have from its first training. The LLM then uses this enriched context to generate a coherent, factual, and contextually relevant answer.

Image Source: Retrieval Augmented Generation by IBM

Optional: Query analysis and orchestration

To further enhance the effectiveness of RAG systems, extra steps can be integrated:

Query Analysis:

Direct user input can be used for retrieval. This allows an LLM to analyze and potentially rewrite the user’s query before retrieval offers significant advantages. This “query rewriting” step can clarify ambiguous queries, fix misspellings, or even add structured filters (e.g., “find documents since 2020”) to improve search accuracy.

For example, a user asking “What’s a good shoe for a mountain trail?” with typos. LLM could have their query rewritten into a more precise search term like “mountain trail shoe.” This leads to much more effective retrieval from a database. This pre-processing of the query is highly effective in production RAG systems.

Orchestration with LangGraph:

For complex RAG applications involving multiple steps, you can use tools like LangGraph.

These tools help orchestrate the flow between retrieval and generation, especially when conditional logic is involved. This setup supports various invocation modes like streaming, asynchronous, and batched calls. It streamlines deployments and enables automatic tracing for debugging and optimization. It also supports persistence for conversational memory or human-in-the-loop approval.

The effectiveness of the RAG system hinges on the quality of data preparation and the precision of the retrieval query.

The process of breaking down large documents into meaningful chunks can be complex. The choice of embedding models affects the semantic accuracy of the search. Additionally, query rewriting highlights the importance of how a user’s question is processed before retrieval. Poorly indexed data or an ineffective retriever can lead to irrelevant context. This could be more detrimental than having no context at all. This emphasizes that successful RAG implementation requires considerable effort and skill in data engineering and retrieval improvement.

Here is a summary of the RAG architecture components and their operational steps:

Phase	Step	Description
1. Indexing Pipeline (Offline)	Load	Ingests raw data from various sources (e.g., PDFs, web pages) using Document Loaders.
	Split	Breaks large documents into smaller, manageable “chunks” using Text Splitters (e.g., `RecursiveCharacterTextSplitter`) to fit LLM context windows and improve search.
	Store	Transforms document chunks into numerical “embeddings” with an Embeddings Model. Next, it stores these embeddings in a VectorStore (Vector Database) and indexes them for efficient similarity search.
2. Retrieval & Generation Chain (Runtime)	Query Analysis (Optional)	An LLM can rewrite or refine the user’s raw query (e.g., fix typos, add structured filters) to improve retrieval accuracy.
	Retrieve	First, it converts the user’s query into an embedding. Then, it uses a Retriever to search the VectorStore. This helps find the most semantically relevant document chunks.
	Generate	It passes the retrieved relevant chunks and the original user query as context to a ChatModel/LLM. The model then produces a coherent answer that is factual and contextually relevant.

RAG in action: RAG examples and use cases

RAG is not merely a theoretical concept. It is actively transforming how AI is deployed across various industries, particularly where precise, up-to-date, and verifiable information is crucial.

Let’s understand more with RAG examples and use cases:

Retrieval Augmented Generation (RAG) for customer support

RAG is perfect for building sophisticated question-answering systems. These systems need to give responses grounded in specific, often proprietary, information.

RAG chatbots access extensive, up-to-date knowledge bases like FAQs, product manuals, and troubleshooting guides. This helps enhance customer satisfaction and lessening the burden on human agents.

For example, if a patient asks, “How do I prepare for my knee surgery?”, a RAG chatbot retrieves specific information directly from the hospital’s documents. It gives a precise and personalized response based on that institution’s procedures. This ensures that replies are relevant and current, benefiting customer service interactions as well.

Here’s a real example: How Thomson Reuters used RAG for customer support and a simpler concise explanation of the same:

Thomson Reuters integrated a Retrieval-Augmented Generation (RAG) pipeline into their technical support workflow. This was done specifically to help customer support agents efficiently access domain-specific knowledge. Here’s how they implemented it:

Two-part RAG workflow at Thomas Reuters

Processing & Indexing Flow

They extracted content from internal knowledge bases (KB articles, CRM systems, support tickets). This text was divided into smaller “chunks” and converted into embeddings using models like BERT, RoBERTa, T5, or OpenAI’s text‑embedding‑ada-002.

These embeddings were then stored in a vector database (dense retrieval system).

Retrieval Flow

The system converts the agent’s query into a vector. It retrieves relevant chunks from the vector DB using semantic similarity. The system passes those retrieved passages to a seq-to-seq LLM. It synthesizes a concise and coherent response grounded in the retrieved context.

In essence, Thomson Reuters transformed their customer support workflow with a custom RAG system. They indexed internal knowledge into embeddings. They retrieved them via semantic search. Additionally, they used LLM-based generation to produce verified, informative, and fast responses for support agents.

Retrieval Augmented Generation (RAG) for Financial Services and Accounting:

The finance and accounting sectors are marked by fast-changing data and strict compliance. RAG enhances accuracy in financial analysis, credit risk assessment, and investment advice by leveraging real-time market data and internal reports.

Key application of RAG for finance:

RAG use cases for financial services and banking sector

Key applications show RAG’s transformative impact across financial operations:

Standardizes information across organizations, reducing inconsistencies in financial reports.
Respond quickly to market and regulatory changes, enabling tailored portfolio recommendations based on current economic data.

Customer service chatbots use RAG to give compliant answers. They incorporate legally reviewed language about lending rules. This ensures strict adherence to regulatory guidelines.
In loan origination, RAG enables automatic generation of term sheets. It uses similar, earlier executed, and legally vetted loans. This process reduces manual work while maintaining accuracy. It also ensures compliance.
Financial advisory tools use RAG to ground investment insights in verified market data. They also rely on internal research. This approach delivers reliable, contextually relevant advice.

RAG for AI governance in finance

Beyond operational efficiency, RAG serves as a foundational pillar of robust AI governance in finance.

It directly supports model risk management by preventing incorrect outputs at the source. It enhances data integrity by ensuring alignment with internal governance controls. It improves transparency through clear audit trails. This transforms potentially risky Gen AI applications into controlled, compliant assets. It enables financial institutions to innovate responsibly. This process maintains the trust and regulatory compliance crucial to the sector.

Limitations of Retrieval Augmented Generation (RAG) for financial services

Nonetheless, the application of RAG in high-stakes domains like finance comes with a critical caution.

While RAG enables use in sensitive areas, it does not inherently guarantee success without rigorous validation and optimization.

Experience shows that the baseline accuracy of a “naive RAG” system with financial data can be as low as 25%. Achieving a realistic goal of 75% accuracy, or even the 86% reported in some scientific papers, requires meticulous attention to detail. Small differences in interpreting key financial data can make a system useless for forecasting or decision-making.

Rag for economic data
byu/Far_Caterpillar8077 inRag

This underscores that for critical applications in finance and accounting, RAG implementation cannot be superficial. It requires a “metrics-driven approach” to RAG operations. This includes continuous monitoring and optimization of data quality. It also involves optimizing retrieval processes and overall performance. This emphasizes the need for expert-level design and ongoing oversight in critical enterprise environments.

RAG vs. other AI techniques: A clear comparison

RAG is a powerful technique, but it is not a one-size-fits-all solution.

Understanding its comparison to other AI optimization techniques is crucial to select the most effective approach for a given task:

Retrieval Augmented Generation (RAG) vs. LLM

Source: Intro to LLM Agents with Langchain: When RAG is Not Enough by Alex Honchar on Medium

The fundamental distinction between RAG and a standalone Large Language Model lies in their access to information.

Without RAG, an LLM generates responses based on its static trained knowledge, limited to its initial training data. It predicts the most probable tokens without storing facts. For specific information, it must be included in the prompt.

RAG introduces an information retrieval component, pulling relevant real-time data from external sources. This allows the LLM to generate informed responses based on current facts, enhancing its output beyond outdated knowledge.

Key differences and use cases of RAG vs LLM

Feature	LLM (Standalone)	RAG (LLM with Retrieval)
Knowledge Base	Primarily relies on its static, pre-trained knowledge base.	Combines its pre-trained knowledge with dynamic, external data retrieval.
Information	May offer outdated or generic information.	Can access and incorporate up-to-date and specialized information.
Accuracy	May sometimes “hallucinate” or give inaccurate responses.	Reduces the risk of hallucinations by grounding responses in retrieved, verifiable data.
Cost & Efficiency	Retraining for new data can be expensive and time-consuming.	More cost-effective for introducing new data without full retraining.
Control & Transparency	Limited developer control over information sources and may lack source attribution.	Offers more developer control over information sources and can give source attribution for greater transparency.
Ideal Use Cases	General text generation, creative writing, translation, summarization, and chatbots where real-time accuracy isn’t paramount.	Applications requiring precise, prompt, and domain-specific information, including customer support, legal research, financial analysis, and knowledge engines.

RAG vs. Fine-Tuning vs. Prompt Engineering

These three techniques all aim to enhance an LLM’s performance. But they differ significantly in their approach, goals, and resource requirements.

Often, RAG, fine-tuning, and prompt engineering are not mutually exclusive and can be merged for optimal outcomes.

RAG vs Prompt Engineering:

Prompt engineering involves crafting input prompts to guide LLM outputs effectively. Typical prompts may include priming instructions (e.g., “you are a plumbing Q&A bot”) and guidance on handling errors or edge cases. This technique is low in resource consumption and allows for manual operation.

It is particularly useful for generating content from scratch in flexible, open-ended scenarios. The goal is to give clear directives without altering the model’s parameters or knowledge base.

While prompt engineering enables rapid prototyping, its effectiveness is constrained by the LLM’s context window.

How is RAG (Retrieval Augmented Generation) different from prompt engineering?

RAG links an LLM to an external database, automating information retrieval to enhance prompts with relevant data. This ensures greater accuracy, especially in customer service chatbots where current and precise information is crucial.

RAG adds dynamic knowledge to the prompt, which the model uses to answer the user’s inquiry.

RAG vs Fine-Tuning:

Fine-tuning process involves retraining a pre-trained LLM on a specific, domain-specific dataset, using examples of prompt-completion pairs.

Fine-tuning adjusts a model’s internal weights through extra training. This enhances its ability to produce desired responses, including specific formatting or tone. The process allows for optimized performance in certain tasks and helps the model learn ‘intuition’ through examples of writing style.

As a result, prompt lengths can decrease, allowing for longer outputs. It can also allow smaller models to carry out comparably to larger ones, improving speed and reducing costs. For instance, switching from GPT-4 to a fine-tuned GPT-3.5 Turbo can yield responses nearly three times faster with about 90% cost savings. Fine-tuning also constrains output variability, reducing unwanted behavior.

Misconceptions about Fine-Tuning:

It’s a misconception that fine-tuning teaches a model facts; models store probabilities, not facts.

For factual recall, RAG is more effective.

Additionally, fine-tuning doesn’t need large datasets, as meaningful results can come from just 20-100 examples with modern models. It’s also less expensive due to parameter-efficient techniques.

Lastly, fine-tuning can complement RAG; they can work together.

How is RAG (Retrieval Augmented Generation) different from Fine-tuning?

Unlike fine-tuning, RAG does not need retraining the LLM to update its knowledge. Instead, updates occur quickly within the external retrieval index. This process makes it more cost-effective for dynamic knowledge bases. RAG allows for real-time updates to the knowledge base, which can then be referenced by the LLM.

Merging RAG, fine-tuning, and prompt engineering strategically

The interplay among AI optimization techniques is crucial for developing effective AI applications. RAG, fine-tuning, and prompt engineering are complementary, not competing alternatives. A sophisticated AI strategy requires understanding each method’s strengths.

For example, an organization might use RAG for factual accuracy from internal documents. They can fine-tune an LLM for a specific brand voice, and apply prompt engineering to improve real-time user interactions. This approach fosters customized AI solutions that are performant and cost-effective, moving away from a “one-size-fits-all” mentality in AI development.

RAG vs Fine-tuning vs Prompt Engineering – Compared

Characteristic	Prompt Engineering	Retrieval Augmented Generation (RAG)	Fine-Tuning
Approach	Optimizes input prompts to steer model output; involves crafting instructions and dynamic content.	Connects LLM to external database for real-time retrieval, adding dynamic knowledge to the prompt.	Retrains LLM on specific datasets using prompt-completion pairs, modifying model weights.
Goals	Steer model output, achieve desired results, prototyping, guide model behavior.	Guide model to more relevant, accurate, current outputs; reduce hallucinations; expand LLM knowledge.	Increase performance in specific tasks; specify formatting/tone; teach “intuition”; reduce prompt length; allow smaller models.
Resource Requirements	Least time-consuming, low compute (manual possible).	Requires data science skill to organize enterprise datasets and construct the data pipelines that connect LLMs to those data sources.	Most demanding (compute-intensive, time-consuming data prep/training), but can be cost-effective with efficient techniques and smaller datasets.
Knowledge Source	Model’s pre-trained knowledge.	External, dynamic, real-time data (e.g., vector database).	Domain-specific datasets (modifies model weights).
Flexibility/Adaptability	Most flexible, good for open-ended generation.	Adaptable to changing data via index updates; ideal for current info.	Less flexible (locked to specific knowledge set; requires retraining for updates), but can be scaled with more examples.
Use Cases	Content generation from scratch, prototyping, refining interaction, defining bot behavior.	Customer service chatbots, Q&A, dynamic data applications, internal knowledge search, providing specific facts.	Sentiment analysis, niche domains, specific style/tone adherence, cost savings on prompts, sales lead qualification.
Relationship	Often combined with RAG/Fine-tuning; limited by context window.	Often combined with Prompt Engineering/Fine-tuning; limited by context window.	Can be enhanced by Prompt Engineering/RAG; compatible with RAG.

RAG vs. AI Agents

While both RAG systems and AI agents enhance Large Language Models, they serve different purposes.

RAG adds external knowledge to LLMs for improved accuracy, acting as a knowledge layer.

In contrast, AI agents empower LLMs to process information and take actions for users. They allow proactive interaction with the world through various tools, incorporating reasoning and planning capabilities.

Incorporating RAG with AI Agents – what is Agentic RAG?

A key distinction is that AI agents can integrate RAG within their architecture to enhance functionality. RAG provides factual grounding and current information for informed decision-making but functions passively, lacking proactive capabilities. Agents using RAG can effectively handle the “knowledge” problem by making sure LLMs access precise information.

AI agents use this knowledge base to do real-world tasks, executing complex activities through reasoning and interacting with external tools.

This combination is termed Agentic RAG. It signifies a shift towards more sophisticated AI systems that actively solve problems rather than just providing information.

RAG vs. Context Retrieval and Semantic Search

Retrieval Augmented Generation (RAG) relies on context retrieval. It uses semantic search to find documents based on meaning rather than just keyword matches. This enables RAG to understand the intent behind queries and return relevant data effectively.

For example, Long-context LLMs, like Claude 2 or Gemini, can continuously process inputs within large context windows. But RAG functions differently. It attaches external documents to the LLM’s input and uses that data for all tasks. This design makes it a scalable architecture for managing dynamic knowledge bases.

While long-context models show significant capacity, RAG offers a structured system for accessing and updating information independently from LLM training. This independence enhances the currency and cost-effectiveness of knowledge management. It makes RAG a more robust solution for enterprise-scale applications and real-time data integration.

Evolving RAG: Advanced models and approaches

The dynamic field of Retrieval Augmented Generation experiences continuous innovations leading to sophisticated models that solve limitations and expand capabilities.

What is self-RAG?

Self-RAG, or Self-Reflective Retrieval-Augmented Generation, symbolizes a significant leap ahead by introducing self-reflection into the RAG process.

Traditional RAG systems might blindly fetch a fixed number of documents. This approach does not consider the query’s complexity or the LLM’s internal knowledge. In contrast, Self-RAG empowers the LLM to make intelligent decisions about retrieval.

Self RAG in AI process cycle diagram by appliedai.tools

This advanced approach allows the model to:

Decide if retrieval is needed: Before fetching external knowledge, Self-RAG checks if extra information is necessary. This ensures precise prompt responses and enhances efficiency by avoiding unnecessary data fetching.
Evaluate the relevance of retrieved passages: Once documents are retrieved, Self-RAG critically assesses their relevance to the query. This ensures that only the most pertinent and reliable information is used.

Critique its own output: The model evaluates its own generated responses for factuality, correctness, and overall quality. This internal critique mechanism allows for iterative refinement, leading to more precise and reliable outputs.

The inference process for Self-RAG involves several steps:

It starts with prompt evaluation using a “Retrieve token.”
Then, documents are selectively retrieved.
Response segments are generated in parallel.
Finally, relevance and support are evaluated with reflection tokens like ISREL (Is Relevant) and ISSUP (Is Supported).

This ability enables Self-RAG to autonomously generate retrieval queries and refine responses, enhancing its intelligence.

Self-reflection in Self-RAG signifies a shift from a passive tool to one capable of active reasoning. It evaluates the necessity and quality of the information, thus improving efficiency and accuracy. This advancement brings RAG systems closer to human-like cognitive processes, making them more robust and trustworthy.

Hybrid retrieval RAG: combining strengths

Traditional RAG systems primarily use vector similarity search for retrieval, which can struggle with specific terms like abbreviations (e.g., GAN, LLaMA) and exact names (e.g., “Biden”). This may hinder precise results.

Hybrid Retrieval-Augmented Generation (RAG) overcomes these limitations by integrating various retrieval approaches within the same pipeline. This usually combines strengths from different indexing schemes.

Semantic Search (Vector Search): Excellent for understanding the contextual meaning of a query and finding conceptually similar documents.
Keyword Search (Sparse Vector Search, e.g., BM25): Ensures precision for exact matches, crucial for abbreviations, proper nouns, or specific technical terms.

By integrating both techniques, Hybrid RAG can overcome the shortcomings of each individually.

A Hybrid RAG system uses semantic search for understanding complex user intent. While, it concurrently executes keyword queries to capture critical exact-match terms. It merges and re-ranks results from these strategies. It uses techniques like “Parallel Retrieval,” which queries multiple indexes at once. It also employs “Cascading Retrieval,” which refines semantic searches with keyword filters.

This development highlights the need for optimizing retrieval across various query types. User queries differ significantly; some need deep semantic understanding, while others seek precise matches. Hybrid RAG goes beyond a single retrieval method. It effectively handles diverse user intents to make sure that information is retrieved accurately, whether semantically or as keywords.

Agentic RAG: proactive problem solvers

I shared about Agentic RAG in RAG vs AI Agents section of this guide. Let’s explore this concept deeper.

Agentic RAG combines the knowledge augmentation capabilities of RAG with the autonomous, goal-oriented behavior of AI agents. This integration moves beyond a reactive question-answering system. It creates an AI that can proactively find information needs. The AI can make decisions and take actions to achieve complex goals.

Traditional RAG versus Agentic RAG in AI - infographic by appliedai.tools

The core pillars of Agentic RAG include:

Autonomy: Unlike traditional RAG systems, Agentic RAG can decide what information is needed. It does not rely on predefined queries or human guidance, even with incomplete datasets or questions needing extra context.
Dynamic Retrieval: It accesses real-time data dynamically. It uses advanced tools like APIs, databases, and knowledge graphs. This ensures its outputs are prompt and precise for current market trends or the latest research.

Augmented Generation: Retrieved data is not just presented. Agentic RAG processes and integrates it with its internal knowledge. This crafts coherent, precise, and contextually tailored responses.
Feedback Loop: The system incorporates feedback into its process. This allows it to refine its responses and adapt to evolving tasks. It continuously improves its performance over time.

Agentic RAG systems enhance decision-making by determining which retrieval strategies to use based on query complexity. They work like intelligent assistants that autonomously solve problems and find necessary information, without merely obeying instructions.

Agentic RAG systems support multi-turn conversations, maintaining context and adapting based on user feedback.

The concept of Agentic RAG marks a significant evolution in AI, integrating retrieval-augmented generation (RAG) with autonomous decision-making and planning. This evolution allows AI to move beyond merely providing information, but to become proactive problem-solvers. As a result, it significantly impacts automation and the development of intelligent assistants across various industries.

The 4 Levels of Retrieval Augmented Generation (RAG) Queries

Research categorizes user queries for RAG systems into four distinct levels.

The categorization is based on the required external data type and the task’s primary focus, which is crucial for optimizing RAG systems for specific use cases:

Explicit Fact Queries: These are direct questions that seek specific factual information. The answer can be found explicitly stated within the retrieved documents.
Implicit Fact Queries: These questions need the LLM to infer or combine multiple facts from the retrieved data. This process helps formulate a full answer. The information might be available, but not in a single, direct statement.
Interpretable Rationale Queries: For these tasks, the reasoning process behind the answer needs to be transparent and explainable. The RAG system must give an answer and show how it arrived at that answer. This is often done by citing specific supporting passages.

Hidden Rationale Queries: These represent the most complex tasks. The underlying reasoning for the answer might be intricate. It could be less obvious, or need sophisticated synthesis of diverse information.

The categorization of RAG queries into four levels shows that RAG implementations vary in effectiveness. Different approaches are necessary for each type of question. Effective RAG design must consider the nature of queries it aims to handle.

Simpler RAG architectures may work for explicit fact queries. Advanced techniques like Self-RAG or Agentic RAG are better suited for implicit or nuanced queries. This understanding helps developers create tailored, efficient RAG systems. These systems meet specific application needs. Developers can avoid opting for a generic solution.

5 key benefits of implementing Retrieval Augmented Generation (RAG) and RAG use cases

Benefits of Retrieval Augmented Generation (RAG) vs LLM comparison - infographic by Appliedai.tools

Let’s focus on the benefits of Retrieval Augmented Generation (RAG):

Improved accuracy and factual grounding

One of RAG’s most compelling benefits is its ability to drastically reduce hallucinations and guarantee responses are factually correct.

RAG provides real-time, verifiable data as part of the input prompt, mitigating the LLM’s tendency to generate inaccurate information. Access to updated, relevant knowledge enhances the reliability of AI-generated content and allows users to verify information sources through citations.

Cost-effectiveness and faster deployment

RAG provides a cost-effective method for deploying AI, particularly for dynamic knowledge bases. It eliminates the need for expensive, resource-heavy retraining of entire LLMs with each new data update. Instead, knowledge can be updated rapidly in the retrieval index, often within minutes. This modularity enables organizations to scale their AI models easily. They simply update or add data to their vector databases. This results in faster deployment and lower operational costs.

Enhanced relevance and adaptability

RAG ensures that generated responses are highly relevant to the specific user query and its context. By dynamically retrieving and synthesizing information from its knowledge base, RAG can acclimate to changing input queries or evolving contexts. This adaptability makes it well-suited for dynamic environments. In these environments, information requirements may shift over time. This ensures that the AI provides precise and contextually appropriate answers.

Explainability and trust

For enterprises, particularly in regulated industries, it is crucial to cite the source of information for building trust. It is also essential for meeting compliance requirements.

RAG provides clear traceability by allowing the system to cite the specific document sources behind its generated sentences. This “lineage” means that every sentence can be tracked back to an immutable and access-controlled source. This offers explainability scores that quantify the depth of evidence for each answer. This transparency is crucial for justifying decisions during audits and maintaining regulatory confidence.

Memory efficiency

RAG uses external databases for vast, fresh information, overcoming LLM’s finite context length.

Here’s a brief table helping you understand how the benefits of RAG translate to business efficiency:

Benefit	Description	Business Impact
Reduced Hallucinations & Increased Accuracy	AI outputs are less likely to be fabricated or incorrect.	Enhances reliability and trustworthiness of AI systems; crucial for risk mitigation in sensitive applications.
Access to Fresh, Up-to-date Information	Incorporates real-time data without needing full model retraining.	Ensures AI responses are current and relevant, supporting agile decision-making and competitive advantage.
Contextual Relevance	Generates responses highly specific to the user’s query and context.	Improves user experience and operational efficiency by providing precise, meaningful answers.
Source Citations & Transparency	Provides links or references to the original data sources.	Builds trust, allows for verification, and supports compliance requirements.
Scalability & Flexibility	Easily adapts to new data or use cases by updating external knowledge.	Reduces development cycles and costs; allows AI applications to grow with business needs.
Memory Efficiency	Leverages external databases to quickly access detailed information.	Overcomes limitations of LLM’s internal memory, enabling richer, more comprehensive responses.

Key limitations or challenges of Retrieval Augmented Generation (RAG)

The implementation of Retrieval Augmented Generation and ongoing operation involve specific challenges. Developers and organizations must tackle these considerations:

Limitation	Description
Hallucination Paradox	Insufficient or misleading context can paradoxically increase hallucination rates, making the model overconfident in incorrect answers.
Reliance on Retrieval Quality	The effectiveness of RAG relies on the quality of the retrieval system and indexed data. Poorly indexed documents can lead to incorrect responses, while irrelevant or insufficient data can make answers off-topic or misleading. This necessitates careful data preparation and continuous improvement of retrieval mechanisms. It helps make sure the relevant context is provided to the LLM.
Increased Complexity/Inference Time	Integrating a retrieval system adds architectural complexity and can increase the time needed to generate a response. For real-time applications, like chatbots, this latency is a significant consideration. Advanced retrieval techniques, like hybrid search, can increase computational expense. Careful design and optimization balance accuracy and performance.
Static Context (Traditional RAG)	Older or simpler RAG models might have limited ability to adapt to dynamic inquiries. They may also struggle with iterative user feedback. In these areas, advanced RAG or agentic systems perform better.
Copyright Protection Risks	The use of proprietary knowledge in RAG systems poses risks of unauthorized usage, requiring strong protection mechanisms. Using RAG systems with sensitive medical or financial data can result in misuse. Research is exploring techniques like watermarking to detect unauthorized use.

Next steps: How to use Retrieval Augmented Generation (RAG)

Here are some good resources I explored to help you learn more about RAG and get started with the available RAG tools:

No-code and low-code RAG tools to get started

Non-technical folks can explore “no-code / low-code solutions” engineered to make the development of RAG applications more accessible and efficient. They focus on “ease of use and quick deployment,” enabling users to start projects without requiring deep technical knowledge.

Several notable platforms exemplify this trend:

Nuclia:

Nuclia – Best for Enterprise-Grade Agentic RAG and AI Search Across Unstructured Data

Nuclia empowers businesses to build AI retrieval pipelines without coding. It offers customizable widgets for embedding RAG functionality into workflows. It can index various file types, including PDFs, PowerPoints, documents, audio, and video. This ensures intuitive information retrieval.

The Nuclia Community Slack provides a platform for technical support. It is also used for community interaction – join Nuclia on Slack

Flowise:

Flowise – Best for Open-Source, Low-Code LLM Agent for Workflow Automation

FlowiseAI is an open-source, no-code/low-code visual development tool. It is designed to help users build AI agents and LLM-driven workflows. These workflows include full Retrieval-Augmented Generation (RAG) pipelines. Users can create them using simple drag-and-drop components.

Make.com Integrations (e.g., with Dumpling AI):

Workflow automation platforms like Make.com are increasingly integrating with RAG tools. Dumpling AI, for example, is developing a RAG tool designed as a module for Make.com. This integration allows users to upload their data, such as PDFs and other documents. They can then pass user queries through the Make.com module to retrieve relevant information chunks, which can then be fed into an LLM. This approach enables “AI-driven precision” with minimal effort.

The Make.com Community Forum is a relevant resource for discussions related to RAG integrations and no-code workflows.

Google Cloud Vertex AI Search:

Described as “Google Search for your data, a fully managed, out-of-the-box search and RAG builder”. While part of a comprehensive cloud platform, its “out-of-the-box” nature suggests a simplified pathway for developing RAG-powered applications.

AWS Bedrock and Azure AI:

These are extensive cloud platforms that offer fully managed services and pre-built implementations for RAG solutions. These platforms typically cater to developers. Nevertheless, their integrated nature can indirectly help teams with some technical support.

How to select a platform for implementing RAG?

When selecting the appropriate platform for RAG implementation, several considerations are paramount:

Ease of Use: For non-technical users, prioritizing platforms with intuitive interfaces and minimal coding requirements is crucial. Features like drag-and-drop functionalities or clear, guided setup processes are highly beneficial.

Data Compatibility: It is essential to guarantee that the chosen platform supports the specific types of data intended for use. This includes documents, databases, audio, or video.
Scalability: Consider whether the platform can accommodate future growth in data volume or user base.
Cost: Evaluate the pricing models carefully. “Off-the-shelf solutions” may involve a premium , while cloud providers often offer flexible pay-as-you-go pricing structures.
Integration: Check if the RAG tool integrates seamlessly with existing business tools. These include customer support systems like Zendesk. It also includes workflow automation platforms like Make.com.
Support and Community: Access to a robust community forum can prove invaluable. Responsive customer support is also particularly helpful during the initial stages of implementation.

Today’s RAG tools remove technical barriers. A wider range of professionals—including marketers, analysts, and customer support team can deploy sophisticated RAG pipelines.

Furthermore, the availability of “off-the-shelf solutions” and “RAG-as-a-Service” offerings reflects a strategic evolution in AI engagement. Many non-technical users can now “configure” pre-built RAG capabilities, aligning them with specific data and use cases. This signifies a shift from building complex AI models from scratch, which requires significant technical investment. As a result, this approach reduces time-to-value and lowers the barrier to AI adoption.

Learn more: Resources for your RAG in AI journey

Several excellent resources give foundational knowledge for those new to RAG and help apply concepts:

“A Beginner’s Guide to RAG: What I Wish Someone Told Me” from Linuxera.org provides a comprehensive outline of practical steps. It guides beginners from understanding RAG’s utility to exploring search strategies. It also covers techniques for improving answer quality. [Read Here]
“Project: Building RAG Chatbots for Technical Documentation” (DataCamp) offers a practical, hands-on opportunity. It lets you implement RAG with LangChain. This project provides valuable real-world experience. [Learn More]
LangChain itself is a robust toolkit designed to integrate language models with external knowledge sources. This integration proves useful for both the retrieval and augmentation stages of RAG. While it leans towards a more code-centric approach, many introductory tutorials are available to ease the learning curve.
Cloud Provider Blogs/Documentation: Major cloud providers like AWS, Azure, and Google Cloud (Vertex AI) offer extensive documentation. They also give regularly updated blogs on RAG implementation and best practices.

At Applied AI Tools, we cover practical adoption of AI concepts like Retrieval Augmented Generation (RAG) and its use cases:

Frequently Asked Questions (FAQs) on Retrieval Augmented Generation (RAG)

What are RAG steps?

The core steps of a Retrieval Augmented Generation (RAG) system include:

Load raw data from sources
Split large documents into smaller, manageable chunks
Store these chunks as numerical embeddings in a vector database (these form the Indexing Pipeline).
Then, at runtime, retrieve relevant chunks based on a user query.
Generate a response using the LLM, augmented by the retrieved context (these form the Retrieval and Generation Chain)

Is ChatGPT a RAG?

No, in its default form, ChatGPT is not a RAG system. It generates answers based on the knowledge it was trained on. This knowledge is stored in its internal weights. It does not dynamically pull real-time information from external data sources. Thus, the free or default paid version of ChatGPT is generally not considered a RAG system.

Is Copilot a Retrieval-Augmented Generation?

Yes, Microsoft Copilot is implemented as a sophisticated RAG system. It uses enterprise data to ground its responses, ensuring they are relevant, precise, and compliant with internal company information.

How to apply retrieval augmented generation (RAG) in AI?

Building an indexing pipeline is a part of applying RAG. This includes loading, splitting, embedding, and storing data in a vector database. Additionally, it involves creating a retrieval-generation chain. At runtime, the system is queried to retrieve relevant context from the vector database. This context is then fed to an LLM for response generation.

How to improve the retrieval process in RAG?

To improve RAG’s retrieval process, start by optimizing data chunking strategies. Use advanced embedding models for better accuracy. Implement query analysis and rewriting techniques to refine user inputs. Use hybrid search techniques that combine semantic and keyword search for comprehensive results.