From Vibe Coding to Context Engineering: A Blueprint for Production-Grade GenAI Systems

Table of Contents

1. Conceptual Foundation: The Evolution of AI Interaction

• 1.1 The Problem Context: Why Good Prompts Are Not Enough
• 1.2 The Historical Trajectory: From Vibe to System
• 1.3 The Core Innovation: The LLM as a CPU, Context as RAM

2. Technical Architecture: The Anatomy of a Context Window

• 2.1 Fundamental Mechanisms: The Four Pillars of Context Management
• 2.2 Formal Underpinnings and Key Challenges
• 2.3 Implementation Blueprint: The Product Requirements Prompt Workflow

3. Advanced Topics: The Frontier of Agentic AI

• 3.1 Variations and Extensions: From Single Agents to Multi-Agent Systems
• 3.2 Current Research Frontiers (Post-2024)
• 3.3 Limitations, Challenges, and Security

4. Practical Applications and Strategic Implementation

• 4.1 Industry Use Cases and Quantifiable Impact
• 4.2 Performance Characteristics and Benchmarking
• 4.3 Best Practices for Production-Grade Context Pipelines

​
5. Resources - my other articles on context engineering

• Context Engineering
• Agentic Context Engineering​
  

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The Evolution of LLM Interaction Paradigms

1. Conceptual Foundation: The Evolution of AI Interaction

1.1 The Problem Context: Why Good Prompts Are Not EnoughThe advent of powerful LLMs has undeniably shifted the technological landscape. Initial interactions, often characterized by impressive demonstrations, created a perception that these models could perform complex tasks with simple, natural language instructions. However, practitioners moving from these demos to production systems quickly encountered a harsh reality: brittleness. An application that works perfectly in a controlled environment often fails when scaled or exposed to the chaotic variety of real-world inputs.1

This gap between potential and performance is not, as is commonly assumed, a fundamental failure of the underlying model's intelligence. Instead, it represents a failure of the system surrounding the model to provide it with the necessary context to succeed. The most critical realization in modern AI application development is that most LLM failures are context failures, not model failures.2 The model isn't broken; the system simply did not set it up for success. The context provided was insufficient, disorganized, or simply wrong.

This understanding reframes the entire engineering challenge. The objective is no longer to simply craft a clever prompt but to architect a robust system that can dynamically assemble and deliver all the information a model needs to reason effectively. The focus shifts from "fixing the model" to meticulously engineering its input stream.

1.2 The Historical Trajectory: From Vibe to SystemThe evolution of how developers interact with LLMs mirrors the maturation curve of many other engineering disciplines, progressing from intuitive art to systematic science. This trajectory can be understood in three distinct phases:

• Prompt Engineering: This was the first major step towards formalizing control over LLMs. The discipline of prompt engineering focuses on the tactical and precise crafting of instructions to elicit a specific, desired output.5 It involves techniques like role-playing, providing few-shot examples, and careful wordsmithing. While a crucial and necessary skill, prompt engineering is a local optimization, focused on perfecting a single turn of an interaction.7 It is now understood to be a small, albeit important, component of a much larger system.9

 

• Vibe Coding: This is the earliest, most intuitive phase of LLM interaction. It is characterized by unstructured, conversational commands, essentially "vibing" with the model to see what it can do.4 This approach is excellent for exploration, rapid prototyping, and discovering a model's latent capabilities. However, it is fundamentally unscalable and unreliable. As a methodology, it "completely falls apart when you try to build anything real or scale it up" because intuition does not scale-structure does.1

 

• Context Engineering: This is the emerging paradigm for building production-grade, reliable, and scalable AI systems. Championed by influential figures like OpenAI's Andrej Karpathy and Shopify's Tobi Lutke, context engineering is a global, architectural discipline.5 It expands the scope of engineering from the prompt alone to the entire context window, treating it as a dynamic resource to be managed. This includes not just the instructional prompt but also chat history, retrieved documents, tool definitions and outputs, user state, and system-level rules.9

This progression from vibe to system is not merely semantic; it signals the professionalization of AI application development. Much like web development evolved from simple, ad-hoc HTML pages to the structured discipline of full-stack engineering with frameworks like MVC, AI development is moving from artisanal prompting to industrial-scale context architecture. The emergence of specialized tools like LangGraph for orchestration and systematic workflows like the Product Requirements Prompt (PRP) system provide the scaffolding that defines a mature engineering field.2

1.3 The Core Innovation: The LLM as a CPU, Context as RAM
​The most powerful mental model for understanding this new paradigm comes from Andrej Karpathy: the LLM is a new kind of CPU, and its context window is its RAM.14 This analogy is profound because it fundamentally reframes the engineering task. We are no longer simply "talking to" a model; we are designing a computational system.

If the LLM is the processor, then its context window is its volatile, working memory. It can only process the information that is loaded into this memory at any given moment. This implies that the primary job of an engineer building a sophisticated AI application is to become the architect of a rudimentary operating system for this new CPU. This "LLM OS" is responsible for managing the RAM-loading the right data, managing memory, and ensuring the processor has everything it needs for the current computational step.
​
This leads directly to Karpathy's definition of the discipline: "In every industrial-strength LLM app, context engineering is the delicate art and science of filling the context window with just the right information for the next step".

2. Technical Architecture: The Anatomy of a Context Window

To move from conceptual understanding to practical implementation, we must dissect the mechanics of managing the context window. The LangChain team has proposed a powerful framework that organizes context engineering operations into four fundamental pillars: Write, Select, Compress, and Isolate.14 These pillars provide a comprehensive blueprint for architecting context-aware systems.

2.1 Fundamental Mechanisms: The Four Pillars of Context Management

1. Write (Persisting State):
This involves storing information generated during a task for later use, effectively creating memory that extends beyond a single LLM call. The goal is to persist and build institutional knowledge for the agent.

• Techniques: Common methods include using a "scratchpad" for intermediate thoughts or chain-of-thought reasoning, logging tool calls and their results to a history, and writing key information to a structured, long-term memory store.11
• Example: A research agent tasked with a complex problem might first formulate a multi-step plan. It writes this plan to a persistent memory object to ensure the plan is not lost if the conversation exceeds the context window's token limit.14

2. Select (Dynamic Retrieval):
This is the process of fetching the right information from external sources and loading it into the context window at the right time. The goal is to ground the model in facts and provide it with necessary, just-in-time information.

• Techniques: The most prominent technique is Retrieval-Augmented Generation (RAG), which retrieves relevant document chunks from a vector database to answer questions or provide factual grounding.5 Other selection techniques include retrieving specific tool definitions based on the task at hand or recalling relevant episodic (past conversations) and semantic (facts about the user) memories.3

3. Compress (Managing Scarcity):
The context window is a finite, valuable resource. Compression techniques aim to reduce the token footprint of information, allowing more relevant data to fit while reducing noise.

• Techniques: This can involve using an LLM to recursively summarize long chat histories or documents. A simpler, heuristic-based approach is trimming, such as removing the oldest messages from a conversation buffer once a certain length is reached.14 A more advanced concept is "Linguistic Compression," which focuses on using informationally dense language to convey maximum meaning in the fewest tokens.20

4. Isolate (Preventing Interference):
This involves separating different contexts to prevent them from negatively interfering with each other. The goal is to reduce noise and improve focus.

• Techniques: A powerful pattern is the use of multi-agent systems, where a complex task is broken down and assigned to specialized sub-agents. Each agent operates with its own isolated, optimized context window, preventing context clash.14 Another technique is sandboxing, where token-heavy or potentially disruptive processes are handled in an isolated environment before their results are selectively passed back to the main context.14

2.2 Formal Underpinnings and Key Challenges
The need for these architectural patterns is driven by fundamental properties and limitations of the Transformer architecture.

1. The "Lost in the Middle" Problem:

• Empirical studies have shown that LLMs tend to pay more attention to information at the very beginning and very end of their context window, with information in the middle having a lower chance of being recalled or utilized effectively.11 This is not an arbitrary flaw but a potential artifact of the underlying attention mechanism. The attention score for a query token qi​ with respect to a key token kj​ is a component of the softmax function Attention(Q,K,V)=softmax(dk​​QKT​)V. The combination of positional encodings and the nature of the softmax distribution can lead to certain positions systematically receiving higher or lower attention, making the placement of information within the context window a critical engineering decision.

2. Context Failure Modes: When context is not properly engineered, systems become vulnerable to a set of predictable failures 11:

• Context Poisoning: Irrelevant, inaccurate, or hallucinated data gets into the context (e.g., via a faulty RAG retrieval) and degrades the reliability of all subsequent generations.
• Context Distraction: The context window is filled with too much clutter or low-signal information, causing the model to lose focus on the primary instruction or task.
• Context Confusion: Superfluous but plausible-sounding context influences the model's response in an incorrect or undesirable way.
• Context Clash: The context contains contradictory information or instructions (e.g., a system prompt says "be concise" but the provided examples are verbose), leading to unstable and unpredictable behavior.

2.3 Implementation Blueprint: The Product Requirements Prompt Workflow
One of the most concrete and powerful implementations of context engineering in practice is the Product Requirements Prompt (PRP) workflow, designed for AI-driven software development. This system, detailed in the context-engineering-intro repository, serves as an excellent case study in applying these principles end-to-end.2

This workflow provides a compelling demonstration of a "Context-as-a-Compiler" mental model. In traditional software engineering, a compiler requires all necessary declarations, library dependencies, and source files to produce a valid executable; a missing header file results in a compilation error. Similarly, an LLM requires a complete and well-structured context to produce correct and reliable output. A missing piece of context, such as an API schema or a coding pattern, leads to a "hallucination," which is the functional equivalent of a runtime error caused by a faulty compilation process.24 The PRP workflow is a system designed to prevent these "compilation errors."

The workflow consists of four main stages:

1. Set Up Global Rules (CLAUDE.md):
This file acts as a project-wide configuration, defining global "dependencies" for the AI assistant. It contains rules for code structure, testing requirements (e.g., "use Pytest with fixtures"), style conventions, and documentation standards. This ensures all generated code is consistent with the project's architecture.2

2. Create Initial Feature Request (INITIAL.md):
This is the "source code" for the desired feature. It is a highly structured document that provides the initial context, with explicit sections for a detailed FEATURE description, EXAMPLES of existing code patterns to follow, links to all relevant DOCUMENTATION, and a section for OTHER CONSIDERATIONS to capture non-obvious constraints or potential pitfalls.2

3. Generate the PRP (/generate-prp):
This is an agentic step where the AI assistant takes the INITIAL.md file as input and performs a "pre-compilation" research phase. It analyzes the existing codebase for relevant patterns, fetches and reads the specified documentation, and synthesizes this information into a comprehensive implementation blueprint-the PRP. This blueprint includes a detailed, step-by-step plan, error handling patterns, and, crucially, validation gates (e.g., specific test commands that must pass) for each step.2

4. Execute the PRP (/execute-prp):
​This is the "compile and test" phase. The AI assistant loads the entire context from the generated PRP and executes the plan step-by-step. After each step, it runs the associated validation gate. If a test fails, the system enters an iterative loop where the AI attempts to fix the issue and re-run the test until it passes. This closed-loop, test-driven process ensures that the final output is not just generated, but validated and working.2

The following table operationalizes the four pillars of context management, mapping them to the specific techniques and tools used in production systems like the PRP workflow.

Core Patterns of Context Engineering

3. Advanced Topics: The Frontier of Agentic AI
As we move beyond single-purpose applications to complex, autonomous agents, the principles of context engineering become even more critical. The frontier of AI research and development is focused on building systems that can not only consume context but also manage, create, and reason about it.

3.1 Variations and Extensions: From Single Agents to Multi-Agent Systems
The orchestration of multiple specialized agents is a powerful application of context engineering, particularly the principle of isolation. Frameworks like LangGraph are designed specifically to manage these complex, often cyclical, workflows where state must be passed between different reasoning units.5 The core architectural pattern is "separation of concerns": a complex problem is decomposed into sub-tasks, and each sub-task is assigned to a specialist agent with a context window optimized for that specific job.14 For example, a "master" agent might route a user query to a "data analysis agent" or a "creative writing agent," each equipped with different tools and instructions.

However, this approach introduces a significant challenge: context synchronization. While isolation prevents distraction, it can also lead to misalignment if the agents do not share a common understanding of the overarching goal. Research from teams like Cognition AI suggests that unless there is a robust mechanism for sharing context and full agent traces, a single-agent design with a continuous, well-managed context is often more reliable than a fragmented multi-agent system.25 The choice of architecture is a critical trade-off between the benefits of specialization and the overhead of maintaining coherence.

3.2 Current Research Frontiers (Post-2024)
The field is advancing rapidly, with several key research areas pushing the boundaries of what is possible with context engineering.

Automated Context Engineering:The ultimate evolution of this discipline is to create agents that can engineer their own context. This involves developing meta-cognitive capabilities where an agent can reflect on its own performance, summarize its own interaction logs to distill key learnings, and proactively decide what information to commit to long-term memory or what tools it will need for a future task.11 This is a foundational step towards creating systems with genuine situational awareness.

Standardized Protocols:
For agents to operate effectively in a wider ecosystem, they need a standardized way to request and receive context from external sources. The development of the Model Context Protocol (MCP) and similar Agent2Agent protocols represents the creation of an "API layer for context".26 This infrastructure allows an agent to, for example, query a user's calendar application or a company's internal database for context in a structured, predictable way, moving beyond bespoke integrations to a more interoperable web of information.

Advanced In-Context Control:
Recent academic research highlights the sophisticated control that can be achieved through context.

• In-Context Exploration: A 2024 NeurIPS paper demonstrated that while LLMs like GPT-4 struggle with complex exploration tasks when given raw historical data, their performance improves dramatically when the context is pre-summarized into key statistics. This proves that the structure and quality of context are paramount for sophisticated decision-making, and simply providing more raw data is not sufficient.28
• In-Context Watermarking (ICW): A May 2025 paper showed that by embedding specific instructions in the prompt, an LLM can be guided to subtly alter its output-for instance, by preferring words that start with certain letters or structuring sentences in an acrostic pattern. This demonstrates a fine-grained level of control over the generative process, achieved entirely through context engineering, and has applications in content provenance and tracking.29

3.3 Limitations, Challenges, and Security
Despite its power, context engineering is not a panacea and introduces its own set of challenges.

The Scalability Trilemma:
There is an inherent trade-off between context richness, latency, and cost. Building a rich context by retrieving documents, summarizing history, and calling tools takes time and computational resources, which increases response latency and API costs.12 Production systems must carefully balance the depth of context with performance requirements.

The "Needle in a Haystack" Problem:
The advent of million-token context windows does not eliminate the need for context engineering. As the context window grows, the "lost in the middle" problem can become more acute, making it even harder for the model to find the critical piece of information (the "needle") in a massive wall of text (the "haystack").11 Effective selection and structuring of information remain paramount.

Security Vulnerabilities: A dynamic context pipeline creates new attack surfaces.

• Context Poisoning: A malicious actor could insert false or misleading information into a knowledge base (e.g., a public wiki) that an agent uses for RAG. The agent would then retrieve this poisoned data and present it as fact, compromising the system's integrity.14
• Indirect Prompt Injection: This is a more insidious attack where a retrieved document (e.g., a webpage or user-submitted file) contains hidden instructions for the LLM. When this document is loaded into the context window, these hidden instructions can hijack the agent's original goal.29

The increasing commoditization of foundation models is shifting the competitive battleground. The strategic moat for AI companies will likely not be the model itself, but the quality, breadth, and efficiency of their proprietary "context supply chain." Companies that build valuable products are doing so not by creating new base models, but by building superior context pipelines around existing ones. Protocols like MCP are the enabling infrastructure for this new ecosystem, creating a potential marketplace where high-quality, curated context can be provided as a service.26 The strategic imperative for businesses is therefore to invest in building and curating these proprietary context assets and the engineering systems to manage them effectively.

​4. Practical Applications and Strategic Implementation
The theoretical principles of context engineering are already translating into significant, quantifiable business value across multiple industries. The ability to ground LLMs in specific, reliable information transforms them from generic tools into high-performance, domain-specific experts.

4.1 Industry Use Cases and Quantifiable Impact
The return on investment for building robust context pipelines is substantial and well-documented in early case studies:

• Legal Tech: Harvey AI, a legal tech unicorn, has built its entire value proposition on context engineering. By creating systems that provide LLMs with context from case law, legal precedents, and client documents, they have reduced legal research time by 75% and document analysis time by 80%.31
• Insurance: Five Sigma, an insurance claims platform, achieved an 80% reduction in errors and a 25% increase in adjuster productivity by implementing AI systems that have real-time access to policy data, claims history, and regulatory information.26
• Scientific Research: The ChemCrow agent demonstrated a 99% reduction in chemical synthesis planning time (from weeks to hours) by integrating 18 specialized chemistry tools, safety protocols, and reaction databases directly into its context.31
• Financial Services: Firms using context-engineered AI for loan decisions have seen error rates drop from 15% to near-zero by ensuring the model has access to all relevant financial data and compliance rules.31
• Broad Impact: Across industries, the implementation of RAG-based context grounding has been shown to reduce hallucination rates by up to 90%. Organizations adopting these principles report 40% reductions in operational costs and a 50% faster time-to-market for new AI initiatives.31

4.2 Performance Characteristics and Benchmarking
Evaluating a context-engineered system requires a shift in mindset. Standard model-centric benchmarks like SWE-bench, while useful for measuring a model's raw coding ability, do not capture the performance of the entire application.32 The true metrics of success for a context-engineered system are task success rate, reliability over long-running interactions, and the quality of the final output.

This necessitates building application-specific evaluation suites that test the system end-to-end. Observability tools like LangSmith are critical in this process, as they allow developers to trace an agent's reasoning process, inspect the exact context that was assembled for each LLM call, and pinpoint where in the pipeline a failure occurred.3

The impact of the system's architecture can be profound. In one notable experiment, researchers at IBM Zurich found that by providing GPT-4.1 with a set of "cognitive tools"-a form of context engineering-its performance on the challenging AIME2024 math benchmark increased from 26.7% to 43.3%. This elevated the model's performance to a level comparable with more advanced, next-generation models, proving that a superior system can be more impactful than a superior model alone.33

4.3 Best Practices for Production-Grade Context Pipelines
Distilling insights from across the practitioner landscape, a clear set of best practices has emerged for building robust and effective context engineering systems.2

• Treat Context as a Product: The knowledge base that feeds your system is not a static asset; it is a living product. It requires version control, automated quality checks to prevent data drift, continuous monitoring, and feedback loops to constantly improve its accuracy and relevance.

 

• Start with RAG, Not Fine-Tuning: For any task that requires external or dynamic knowledge, RAG should be the default starting point. It is generally cheaper, faster to implement, and more transparent than fine-tuning. Reserve fine-tuning for teaching the model a specific skill, behavior, or style that cannot be achieved through prompting or RAG, not for injecting factual knowledge.

 

• Structure Prompts for Clarity: The final assembly of the context window matters. Place high-level instructions and the model's persona at the very beginning. Use clear separators (e.g., ### or XML tags) to delineate between instructions, retrieved context, examples, and the user's query. To combat the "lost in the middle" problem in very long contexts, a common pattern is to place large blocks of retrieved information first, followed by the specific question or instruction, forcing the model to process the knowledge before seeing the task.

 

• Be Explicit and Comprehensive: Do not assume the model knows your project's conventions or constraints. Provide explicit rules, comprehensive examples of both what to do and what not to do, and links to all necessary documentation.

 

• Iterate Relentlessly: Building a great context-aware system is an iterative process. Continuously experiment with and A/B test different chunking strategies, embedding models, retrieval methods, and prompt structures. Measure performance against a well-defined evaluation suite and refine the system based on empirical data.

This strategic approach, particularly the "RAG first" principle, has significant financial implications for organizations. Fine-tuning a model is a large, upfront Capital Expenditure, requiring immense compute resources and specialized talent. In contrast, building a context engineering pipeline is primarily an Operational Expenditure, involving ongoing costs for data pipelines, vector database hosting, and API inference.24 By favoring the more flexible, scalable, and continuously updatable OpEx model, organizations can lower the barrier to entry for building powerful, knowledge-intensive AI applications. This reframes the strategic "build vs. buy" decision for technical leaders: the question is no longer "should we fine-tune our own model?" but rather "how do we build the most effective context pipeline around a state-of-the-art foundation model?"

5. Resources

Core

• Andrej Karpathy's X (Twitter) post endorsing "context engineering".1

• Tobi Lutke's X (Twitter) post on the descriptive power of the term.10
• LangChain Blog: "The rise of 'context engineering'" 3 and "Context Engineering for Agents".14
• Sundeep Teki: "Context Engineering: A Framework for Robust Generative AI Systems".24
• Can large language models explore in-context? (Krishnamurthy et al., 2024).28
• In-Context Watermarks for Large Language Models (Zhu et al., 2025).29
• Thus Spake Long-Context Large Language Model (Survey, 2025).34​

 
Citations

1. Context Engineering is the New Vibe Coding (Learn this Now) - YouTube, https://www.youtube.com/watch?v=Egeuql3Lrzg
2. coleam00/context-engineering-intro: Context engineering is the new vibe coding - it's the way to actually make AI coding assistants work. Claude Code is the best for this so that's what this repo is centered around, but you can apply this strategy with any AI coding assistant! - GitHub, https://github.com/coleam00/context-engineering-intro
3. The rise of "context engineering" - LangChain Blog, https://blog.langchain.com/the-rise-of-context-engineering/
4. Building Websites and Web Apps Without Code Just Got Better with Hostinger Horizons, https://analyticsindiamag.com/ai-trends/building-websites-and-web-apps-without-code-just-got-better-with-hostinger-horizons/
5. Context Engineering is the New Vibe Coding, https://analyticsindiamag.com/ai-features/context-engineering-is-the-new-vibe-coding/
6. A Deep Dive into Prompt Engineering Techniques: Part 1 - OmbuLabs, https://www.ombulabs.com/blog/prompt-engineering-techniques-part-1.html
7. Context Engineering vs Prompt Engineering | by Mehul Gupta | Data Science in Your Pocket, https://medium.com/data-science-in-your-pocket/context-engineering-vs-prompt-engineering-379e9622e19d
8. Context Engineering vs Prompt Engineering : r/ChatGPTPromptGenius - Reddit, https://www.reddit.com/r/ChatGPTPromptGenius/comments/1lmnj1j/context_engineering_vs_prompt_engineering/
9. Context Engg vs Prompt Engg | Andrej Karpathy termed. | by NSAI | Jun, 2025 | Medium, https://medium.com/@nisarg.nargund/context-engg-vs-prompt-engg-andrej-karpathy-termed-7ee3f9324114
10. Context engineering - Simon Willison's Weblog, https://simonwillison.net/2025/Jun/27/context-engineering/
11. Context Engineering Is the Real Work of AI - BizCoder, https://bizcoder.com/context-engineering-is-the-real-work-of-ai/
12. Context Engineering: The Next Frontier in AI Usability and Performance | by Md Mazaharul Huq | Jun, 2025 | Medium, https://medium.com/@jewelhuq/context-engineering-the-next-frontier-in-ai-usability-and-performance-c71bee6f8f7b
13. LangGraph - LangChain, https://www.langchain.com/langgraph
14. Context Engineering - LangChain Blog, https://blog.langchain.com/context-engineering-for-agents/
15. Context Engineering : r/LocalLLaMA - Reddit, https://www.reddit.com/r/LocalLLaMA/comments/1lnldsj/context_engineering/
16. Context Engineering for Agents - YouTube, https://www.youtube.com/watch?v=4GiqzUHD5AA
17. Are Large Language Models In-Context Graph Learners? - arXiv, https://arxiv.org/abs/2502.13562
18. AI Dev 25 | Harrison Chase: Long Term Memory with LangGraph - YouTube, https://www.youtube.com/watch?v=R0OdB-p-ns4
19. Context Engineering - What it is, and techniques to consider - LlamaIndex, https://www.llamaindex.ai/blog/context-engineering-what-it-is-and-techniques-to-consider
20. Context Engineering tutorials for beginners (YT Playlist) : r/PromptEngineering - Reddit, https://www.reddit.com/r/PromptEngineering/comments/1low4l1/context_engineering_tutorials_for_beginners_yt/
21. What's Context Engineering and How Does it Apply Here? : r/ArtificialSentience - Reddit, https://www.reddit.com/r/ArtificialSentience/comments/1lnxrl0/whats_context_engineering_and_how_does_it_apply/
22. Context Engineering - LangChain Blog, https://blog.langchain.dev/context-engineering-for-agents/
23. Context Engineering - The Hottest Skill in AI Right Now - YouTube, https://www.youtube.com/watch?v=ioOHXt7wjhM
24. Context Engineering: A Framework for Robust Generative AI Systems - Sundeep Teki, https://www.sundeepteki.org/blog/context-engineering-a-framework-for-robust-generative-ai-systems
25. Context Engineering: Elevating AI Strategy from Prompt Crafting to Enterprise Competence | by Adnan Masood, PhD. | Jun, 2025 | Medium, https://medium.com/@adnanmasood/context-engineering-elevating-ai-strategy-from-prompt-crafting-to-enterprise-competence-b036d3f7f76f
26. Context is Everything: The Massive Shift Making AI Actually Work in the Real World, https://www.philmora.com/the-big-picture/context-is-everything-the-massive-shift-making-ai-actually-work-in-the-real-world
27. Anatomy of a Context Window: A Guide to Context Engineering - Letta, https://www.letta.com/blog/guide-to-context-engineering
28. Can large language models explore in-context?, https://arxiv.org/abs/2403.15371
29. In-Context Watermarks for Large Language Models - arXiv, https://arxiv.org/abs/2505.16934
30. Context Engineering: The Future of AI Prompting Explained - AI-Pro.org, https://ai-pro.org/learn-ai/articles/why-context-engineering-is-redefining-how-we-build-ai-systems/
31. Context Engineering: The Game-Changing Discipline Powering Modern AI, https://dev.to/rakshith2605/context-engineering-the-game-changing-discipline-powering-modern-ai-4nle
32. Claude 4 benchmarks show improvements, but context is still 200K - Bleeping Computer, https://www.bleepingcomputer.com/news/artificial-intelligence/claude-4-benchmarks-show-improvements-but-context-is-still-200k/
33. davidkimai/Context-Engineering: "Context engineering is the delicate art and science of filling the context window with just the right information for the next step." - Andrej Karpathy. A practical, first-principles handbook inspired by Karpathy and 3Blue1Brown for moving beyond prompt engineering to the wider discipline of context design, orchestration - GitHub, https://github.com/davidkimai/Context-Engineering
34. Thus Spake Long-Context Large Language Model, https://arxiv.org/abs/2502.17129
35. Context Engineering : Andrej Karpathy drops a new term for Prompt Engineering after "vibe coding." : r/PromptEngineering - Reddit, https://www.reddit.com/r/PromptEngineering/comments/1llj2ro/context_engineering_andrej_karpathy_drops_a_new/
36. Context Engineering - Simply Explained | by Dr. Nimrita Koul | Jun, 2025 | Medium, https://medium.com/@nimritakoul01/context-engineering-simply-explained-76f6fd1c04ee