Context engineering is an important skillset to make truly useful AI Agents that get the task done. I have started to compile a list of all useful resources for the same. These resources are from blogs, tweets, tutorials, lectures, etc. You can bookmark them to learn context engineering on your own.
If you’re new to context engineering basics, I have published a beginner-friendly blog post on ‘What is Context Engineering’. This includes examples of how OpenAI, Anthropic and LangChain have adopted context engineering respectively:
Learning path recommendations for the learner
To master this field, the learner should adopt a structured approach:
- Deconstruct the Paradigm: Start with the non-technical literature to deeply understand the ‘LLM as OS’ metaphor.
- Master the Data: Spend time on the basics of Markdown formatting and chunking strategies.
- Build the Pipeline: Implement a RAG system using no-code tools before moving to Python frameworks like LangChain.
- Measure Everything: Adopt RAGAS early. Do not engineer in the dark.
- Study the Architectures: Read the MemGPT and Ring Attention papers to understand the future of memory and scaling.
By following this path, the practitioner transitions from a prompt writer to a context architect. They become capable of building reliable, stateful, and intelligent systems. These systems define the next generation of AI.
Summary Table: Comprehensive Resource Path
| Level | Focus Area | Primary Concept | Recommended Resource |
| Non-Technical | Conceptual | Prompt vs. Context | Basics of Context Engineering |
| Non-Technical | Industry Trends | The “Context” Shift | Sam Willison |
| Beginner | Implementation | Data Formatting | Markdown vs JSON for Embeddings |
| Beginner | Practice | Basic RAG | No-Code RAG with n8n |
| Beginner | Practice | Course & Templates | David Kimai |
| Beginner | Guide | Context Definitions | Phil Schmid |
| Intermediate | Optimization | Context Caching | Gemini API Context Caching |
| Intermediate | Evaluation | Metrics (Faithfulness) | RAGAS Documentation |
| Advanced | Architecture | OS/Memory Paging | MemGPT Research Paper |
| Advanced | Scaling | Infinite Context | Ring Attention (Akasa/arXiv) |
| Academic | Theory | Retrieval Systems | Stanford CS25: Retrieval Augmented LMs |
Explainer blog posts on what is context engineering
Here’s a list of important blog post to explore and official explainers by leading AI models on the same:
Learn context engineering by Sam Willison
Simon Willison’s post advocates moving beyond prompt engineering. He calls this new approach ‘context engineering.’ It is the art and science of filling the context window for an AI agent with just the right information. The key is not too much and not too little.
Willison details how context engineering includes dynamic strategies. These strategies involve using state, history, and few-shot learning. RAG, tool/data selection, and context compaction techniques are also included. All these aim to optimize LLM performance for industrial applications. He cites specific tactics like context pruning, tool loadout, and summarization. He also mentions offloading. He concludes that “context is not free.” Mastering its management is now vital for anyone building robust, real-world AI systems.
Learn more: Context Engineering – Simon Willison
Phil Schmid: The New Skill in AI is Not Prompting, It’s Context Engineering
Phil Schmid argues that the new core skill for AI is context engineering. This involves designing dynamic, system-level pipelines. These pipelines supply the LLM with all needed information and tools at just the right time and in the optimal format. He breaks down context into system prompts, user requests, memory, retrieved knowledge, tools, and output structure. A rich, well-structured context transforms an agent from mediocre to ‘magical.’
Schmid demonstrates that most failure points in AI agents are due to poor context, not model errors. Effective context engineering, he concludes, is essential to move from demos to production-grade AI.
Learn more: The New Skill in AI is Not Prompting, It’s Context Engineering
FlowHunt: The Definitive 2025 Guide to Mastering AI System Design
This guide covers foundational and advanced context engineering principles, from distinguishing prompt vs. context to handling memory decay (“context rot”), multi-agent orchestration, and continuous context optimization. Readers learn key strategies for maintaining long-term, reliable AI workflows and applying context engineering to real-world, complex systems.
Learn more: FlowHunt Context Engineering Guide
Context Engineering Tutorials to Practice
Towards Data Science: Comprehensive Hands-On Tutorial
This in-depth tutorial introduces ‘context engineering’ as the foundation for robust LLM application development.
Using the DSPy framework, it offers step-by-step code walkthroughs and visual explanations. These help in building modular, multi-agent workflows. The process includes breaking down tasks, designing context pipelines, and integrating memory. It also involves RAG, structured outputs, and tool actions.
The article features practical scenarios like orchestrating agents to generate, refine, and format outputs. It also addresses real-world concerns like failure modes, evaluation metrics, and cost/latency monitoring. Every concept is paired with runnable code, links to an open-source repository, and a full companion YouTube video course. This makes it accessible for both novice and experienced builders.
Learn more: Context Engineering — A Comprehensive Hands-On Tutorial
Codecademy: Context Engineering in AI Implementation Guide
Codecademy’s interactive guide provides a hands-on approach to mastering context engineering in AI development.
The course walks learners through analyzing use cases. It helps in selecting the most effective context strategies— like Retrieval-Augmented Generation (RAG), memory design, and tool integration. It also involves organizing context layers like user history, domain data, and dynamic updates.
Step-by-step, it demonstrates building practical context architectures:
- Creating retrieval pipelines
- Ranking systems
- Memory modules to deliver focused, relevant information to LLMs.
You learn to apply validation mechanisms to keep context accurate. Practice filtering techniques to avoid overload and use measurable metrics (like response accuracy and user satisfaction) to refine approach. The focus is on developing secure, context-aware programs with iterative, real-world projects for conversational, knowledge, and multi-tool AI systems.
Learn more: Context Engineering in AI: Complete Implementation Guide.
Learn context engineering approaches from leading AI models or framework documentation
In this section, you can explore how leading AI models and frameworks are implementing context engineering in their ecosystems:
LangChain: Context Engineering for Agents
LangChain’s blog and documentation offer a deep technical dive into the ‘art and science’ of managing agent context at every workflow step. Tutorials focus on four core context engineering strategies: writing, selecting, compressing, and isolating context. The LangGraph framework shows how to checkpoint agent state. It swaps between short- and long-term memory. Additionally, it applies modular context reducers and summarizers.
Learn more: Context Engineering for Agents – LangChain Blog
The docs provide a practical breakdown of controlling model, tool, and life-cycle context. They include hooks (middleware) for updating state between agent steps. There is also persistent memory, advanced guardrails, and context-dependent execution logic. Highly recommended for anyone building AI agents and tool-integrating LLMs.
They have also published a dedicated blog on understanding how agents use context engineering if you prefer video content:
It includes demos from top agent frameworks like Claude and DeepAgents. You see how context engineering enables agents to handle large data, minimize irrelevant info, and reliably ‘think in tokens.’ Real-world scenarios show segmenting knowledge, applying compact summaries, and switching subagent focus for improved reliability at scale.
GitHub: Context Engineering Handbook (David Kimai)
This open-source handbook goes beyond prompt engineering, focusing on first-principles design and orchestration for broader, persistent context management. It compiles code examples and provides best practices for context optimization. The handbook also covers advanced topics like cross-agent communication, symbolic context modeling, and evaluation. It addresses emerging concepts like neural field theory and quantum semantics. Designed for both newcomers and advanced builders, the handbook is a living, regularly updated resource.
Explore: Context Engineering Handbook
Known for its ‘beyond prompt engineering’ ethos, this handbook offers code, guides, and uncommon insights from community and industry leaders.
Anthropic: Effective Context Engineering for AI Agents
Anthropic’s engineering blog shares in-depth techniques used to improve context delivery in AI agents. It highlights the use of “context windows” and “pooling” for effective memory usage, methods for chaining prompts, and multi-step reasoning. The post discusses reducing hallucinations with layered context management and combining retrieval with generative memory.
Learn more: Effective Context Engineering for AI Agents
Lectures that dive deep to learn context engineering
Here are some good lectures I found that cover the topic to learn context engineering:
Context Engineering SF – August 2025 by YCombinator
Level: Intermediate
This industry-focused YouTube playlist looks at context management techniques from leading AI companies, showing lessons learned in actual production environments. It includes 4 videos explaining context engineering by YCombinator experts.
Stanford CS25: Retrieval Augmented Language Models
Level: Advanced
The Stanford CS25 seminar series features a seminal lecture by Douwe Kiela on Retrieval Augmented Language Models. This lecture is foundational. It helps in understanding the transition from ‘Parametric Memory,’ which is knowledge stored in the model’s weights, to ‘Non-Parametric Memory,’ which is knowledge stored in external retrieval indices.
Kiela argues that retrieval augmentation is not just a hack to fix hallucinations. Instead, it is a fundamental architectural improvement. It decouples ‘reasoning’ (the model) from ‘knowledge’ (the index). This allows for knowledge to be updated instantly without re-training the model—a concept central to modern context engineering. The lecture covers the history from early RAG papers to advanced ‘Atlas’ and ‘RETRO’ architectures. This examination provides the academic lineage of the tools developers use today.
MIT 6.S091: Introduction to Deep Learning and LLMs
Level: Advanced
MIT’s course offers a broader systems view. Lectures by Peter Grabowski (Google) and Maxime Labonne cover the scaling laws that dictate context window performance.
A key takeaway from these lectures is the relationship between Context Length and Reasoning. The curriculum highlights that while models can technically accept long contexts, their ability to reason over that context degrades non-linearly. This reinforces the engineering necessity of RAG and compression even as context windows grow.
The lectures also cover ‘Prompt Tuning’ and ‘Soft Prompts’. These are techniques where the context itself is optimized via gradient descent. This approach is different from manual text editing. It signifies the ‘calculus’ end of the context engineering spectrum.
UC Berkeley CS288/294: Large Language Model Agents
The Berkeley curriculum, particularly the ‘LLM Agents’ series, focuses on the agentic aspect of context. The lectures explore how context serves as the “state” for an agent acting in an environment.
Learn more: CS 288 and Introduction to training LLMs for AI agents
Instructors discuss the ‘Data Wall’ and the limitations of training data. They position context engineering (via RAG and tool use) as the primary method to overcome these limits for enterprise applications. The course delves into ‘Inference-time techniques,’ validating the industry move toward ‘thinking tokens’ and iterative context refinement. It provides the theoretical basis for why multi-agent systems with specialized contexts often outperform single, massive-context models.
Temporal.io Webinar: Simplifying Context Engineering for AI Agents in Production
Level: Advanced
A vendor-neutral practical webinar covering common context engineering challenges and solutions. You learn how to build reliable, focused agents with efficient context pipelines. These pipelines integrate memory, retrieval, and tool use. The webinar provides actionable tactics for production deployment.
Share a good resource you find to learn context engineering
This blog post will get updated as I continue to find good resources to learn context engineering. If you have found or published something, do email to content@merrative.com to get listed.
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