 Source: https://www.dbreunig.com/2025/06/25/prompts-vs-context.html Table of Contents (last revised: 14 July, 2025) 1. Conceptual Foundations: From Prompt to System
2. The Architectural Blueprint of Context-Aware Systems
3. Advanced Context Engineering Techniques
4. Advanced Frontiers and State-of-the-Art Techniques (2025)
5. Practical Implementation and Performance
6. Failures of Context 7. Resources Follow-up blog post: From Vibe Coding to Context Engineering In this guide, I synthesize insights from foundational blog posts on the emergence of Context Engineering, the seminal Lewis et al. paper on Retrieval-Augmented Generation (RAG), and a vast corpus of recent (2024-2025) research on advanced topics like Agentic RAG, Context Compression and the "Context-as-a-Compiler" mental model. Context Engineering is not an extension of prompt engineering but a distinct system-level discipline focused on creating dynamic, state-aware information ecosystems for AI agents. The key conclusion is that the frontier of AI application development has shifted from model-centric optimization to context-centric architecture design. The most capable models underperform not due to inherent flaws, but because they are provided with an incomplete, "half-baked view of the world". This guide provides the architectural blueprints, advanced techniques, and practical frameworks necessary to master this critical new discipline.  Source: https://www.philschmid.de/context-engineering 1. Conceptual Foundations: From Prompt to System The discourse surrounding Large Language Models (LLMs) has historically been dominated by model scale and prompt design. However, as the capabilities of foundational models begin to plateau, the critical differentiator for building effective, reliable, and "magical" AI applications has shifted from the model itself to the information ecosystem in which it operates. This section establishes the fundamental paradigm shift from the tactical act of writing prompts to the strategic discipline of engineering context, grounding the practitioner in the core principles that motivate this evolution. 1.1. Defining the Paradigm: The Rise of Context Engineering Context Engineering is the discipline of designing, building, and optimizing the dynamic information ecosystem provided to an AI model to perform a task. It represents a fundamental evolution from the stateless, single-turn world of prompt engineering to the stateful, multi-turn environment of sophisticated AI systems. While prompt engineering focuses on crafting the perfect instruction, context engineering architects the entire world of knowledge the model needs to interpret that instruction correctly and act upon it effectively. This engineered context is a composite of multiple information streams, including but not limited to:
1.2. The "Context is King" Paradigm: Why World-Class Models Underperform A persistent and uncomfortable truth in applied AI is that the quality of the underlying model is often secondary to the quality of the context it receives. Many teams invest enormous resources in swapping out one state-of-the-art LLM for another, only to see marginal improvements. The reason is that even the most powerful models fail when they are fed an incomplete or inaccurate view of the world. The core limitation of LLMs is their reliance on parametric knowledge - the information encoded in their weights during training. This knowledge is inherently static, non-attributable, and lacks access to private, real-time, or domain-specific information. When a model is asked a question that requires information beyond its training cut-off date or about a proprietary enterprise database, it is forced to either refuse the query or, more dangerously, "hallucinate" a plausible-sounding but incorrect answer. Context Engineering directly addresses this fundamental gap. It is the mechanism for providing the necessary grounding to ensure factual accuracy, relevance, and personalization. Consider a simple task: scheduling an email. A prompt like "Email Jim and find a time to meet next week" sent to a generic LLM will yield a generic, unhelpful draft. However, a system built with context engineering principles would first construct a "contextual snapshot". This snapshot would include:
By feeding this rich context to the same LLM, the system can generate a "magical" and immediately useful output, such as: "Hey Jim! Tomorrow's packed on my end, back-to-back all day. Thursday AM free if that works for you? Sent an invite, lmk if it works". The model did not get "smarter"; its environment did. This illustrates the core principle: the value is unlocked not by changing the model, but by fixing the context. 1.3. The "Context-as-a-Compiler" Analogy: A New Mental Model for Development A powerful mental model for understanding this new paradigm is the "Context-as-a-Compiler" analogy, a concept discussed by leading researchers like Andrej Karpathy. This model reframes the LLM as a new kind of compiler that translates a high-level, often ambiguous language (human intent expressed in natural language) into a low-level, executable output (e.g., code, API calls, structured JSON). In this analogy, the prompt is not just a question; it is the source code. The context is everything else the compiler needs to produce a correct, non-hallucinated binary. This includes the equivalent of:
The goal of context engineering, therefore, is to make the compilation process as deterministic and reliable as possible. A traditional C++ compiler will fail if a function is called without being declared; similarly, an LLM will "hallucinate" if it is asked to operate on information it does not have. Context engineering is the practice of providing all the necessary declarations and definitions within the context window to constrain the LLM's stochastic nature and guide it toward the correct output. This analogy also illuminates a fundamental shift in the developer workflow. When code generated by a tool like GitHub Copilot is wrong, developers often do not debug the incorrect Python code directly. Instead, they "fiddle with the prompt" or adjust the surrounding code until the generation is correct. In the compiler analogy, this is equivalent to modifying the source code and its dependencies. The context is the new debuggable surface. This implies that the primary skill for the AI-native developer is not just writing the final artifact (the code) but curating and structuring the context that generates it. The development environment of the future may evolve into a "context IDE," where developers spend more time managing data sources, retrieval strategies, and agentic workflows than editing lines of code. The rise of "vibe coding" - describing the "vibe" of what is needed and letting the AI handle the implementation - is a direct consequence of this new layer of abstraction. However, the analogy has its limits. Unlike a traditional compiler, which is a deterministic tool, an LLM is a stochastic system that can creatively resolve ambiguity. This is both its greatest strength and its most significant weakness. While a compiler will throw an error for ambiguous code, an LLM will make its best guess, which can lead to unexpected (and sometimes brilliant, sometimes disastrous) results. The art of context engineering lies in providing enough structure to ensure reliability while leaving just enough room for the model's powerful generative capabilities to shine. 1.4 Deterministic vs. Probabilistic Context A crucial distinction within context engineering is between deterministic and probabilistic context.
The introduction of probabilistic context presents significant engineering challenges. The quality, reliability, and factuality of the information are not guaranteed. It dramatically increases the system's vulnerability to security risks, such as LLM injection attacks, where malicious content retrieved from an external source can manipulate the model's behavior. Furthermore, traditional evaluation metrics like precision and recall become less effective, as the "correct" context is not known a priori. Engineering robust systems therefore requires a focus on shaping the agent's exploration of this probabilistic space, monitoring the quality of information sources, and implementing rigorous security precautions. 2. The Architectural Blueprint of Context-Aware Systems Moving from conceptual foundations to technical implementation, this section details the architectural patterns and components that form the backbone of modern context-engineered systems. These blueprints provide the "how" that enables the "why" discussed previously, focusing on the core mechanisms for grounding LLMs in external knowledge. 2.1. The Foundational Pattern: Retrieval-Augmented Generation (RAG) Retrieval-Augmented Generation (RAG) is the cornerstone pattern of context engineering. Introduced in the seminal 2020 paper by Lewis et al., RAG was designed to combine the strengths of parametric memory (knowledge stored in model weights) and non-parametric memory (an external knowledge base). RAG was proposed as a general-purpose recipe to address these issues by combining the implicit, parametric memory of a pre-trained model with an explicit, non-parametric memory in the form of a retrievable text corpus. The core innovation was to treat the retrieved document as a latent variable, enabling the retriever and generator components to be trained jointly, end-to-end. While LLMs demonstrated a remarkable ability to internalize knowledge from their training data, they suffered from several critical flaws:
The standard RAG process consists of three primary stages :
This entire pipeline serves as the fundamental mechanism for context engineering, providing a structured way to inject relevant, external knowledge into the LLM's reasoning process at inference time. 2.2. The Critical Decision: RAG vs. Fine-Tuning A primary strategic decision facing any team building with LLMs is whether to use RAG, fine-tuning, or both. These methods address different problems and have distinct trade-offs in terms of cost, complexity, and capability. Choosing the correct path is crucial for project success.
The consensus among practitioners is to start with RAG by default. It is faster, cheaper, and safer for most use cases involving factual knowledge. Fine-tuning should only be considered when RAG proves insufficient to achieve the desired behavior or when the task is purely about style and not knowledge.  A decision matrix to guide the choice of RAG vs. Fine-tuning vs. Hybrid systems 2.3. The Hybrid Approach: The Best of Both Worlds While RAG and fine-tuning are often presented as an either/or choice, the most sophisticated systems frequently employ a hybrid approach to achieve performance that neither method can reach in isolation. This strategy recognizes that the two methods are complementary: RAG provides the facts, while fine-tuning teaches the skill of using those facts. The standard RAG approach relies on a general-purpose base model to synthesize an answer from the retrieved context. However, this base model may not be optimized for this specific task. It might struggle to identify the most salient points in the context, ignore contradictory evidence, or fail to structure the output in the desired format. A hybrid approach addresses this by fine-tuning the generator model specifically to be a better RAG component. The fine-tuning dataset in this case would not consist of just questions and answers. Instead, each example would be a triplet of (query, retrieved_context, ideal_answer). The goal of this fine-tuning is not to bake the knowledge from the retrieved_context into the model's weights. Rather, it is to teach the model the skill of faithfully synthesizing a high-quality answer from whatever context it is given. This fine-tuning can teach the model to:
The optimal architecture for many complex enterprise applications is therefore a model that has been fine-tuned for "contextual reasoning and synthesis," coupled with a powerful and dynamic RAG pipeline. This allows the system to learn the desired style and structure via fine-tuning, while dynamically populating its responses with up-to-date facts from RAG. 2.4 The Tech Stack for Context Engineering Building production-grade, context-aware systems relies on a maturing ecosystem of specialized tools and frameworks. Mastering this tech stack is as important as understanding the conceptual architecture.
 3. Advanced Context Engineering Techniques As LLM applications move from simple prototypes to robust production systems, developers quickly discover that a naive RAG implementation is often insufficient. The quality of an LLM's output is exquisitely sensitive to the quality of its context. Therefore, a significant portion of engineering effort is dedicated to advanced techniques for curating this context. These strategies can be grouped into four core patterns: writing, selecting, compressing, and isolating information. 3.1 Write: Contextual Memory Architectures LLMs are inherently stateless; they have no memory of past interactions beyond what is explicitly provided in the current context window. Building coherent, multi-turn applications requires architecting an external memory system. This is the "Write" pattern: saving state and learned information for future reference.
3.2 Select: Advanced Retrieval and Filtering The "Select" pattern focuses on improving the signal-to-noise ratio of the information retrieved for the context window. The goal is to move beyond naive vector similarity and retrieve documents that are not just semantically similar but truly useful for answering the user's query.
3.3 Compress: Managing Million-Token Windows The advent of million-token context windows did not eliminate the need for context engineering; paradoxically, it intensified it. A larger window is not a bigger brain but a bigger, noisier room that requires a more sophisticated librarian. Research has consistently shown that LLMs suffer from a "lost in the middle" problem, where their ability to recall information is highest at the beginning and end of the context window and significantly degrades for information buried in the middle.11 Furthermore, processing long contexts is computationally expensive and slow. This makes naive context-stuffing both ineffective and inefficient. The most advanced systems recognize that architecture trumps raw context size; a well-designed RAG system with a smaller, curated context can outperform a naive system with a million-token window. The "Compress" pattern is therefore critical for managing these large contexts effectively.
3.4 Isolate: Compartmentalizing Context Mixing unrelated information streams into a single context window - a "context soup" - is a recipe for failure. It can lead to several distinct problems:
The "Isolate" pattern addresses this by strictly compartmentalizing context. Different tasks, sub-agents, or conversational threads should operate within their own isolated context windows. This is typically managed by the orchestration layer. Frameworks like LangGraph, for example, are designed around a central state object. For each independent workflow or session, a separate state object is maintained. The logic of the graph ensures that at any given step, only the relevant parts of that state (e.g., the current sub-task's instructions, the relevant memory) are passed into the LLM's context, preventing interference from other, unrelated processes. 4. Advanced Frontiers and State-of-the-Art Techniques (2025) The field of Context Engineering is evolving at a breathtaking pace. Beyond the foundational RAG pattern, a new frontier of autonomous, efficient, and structured techniques is emerging. This section explores the state-of-the-art developments that are defining production-grade AI systems in 2025 and beyond. 41. The Agentic Leap: From Static Pipelines to Autonomous Systems The most significant evolution in context engineering is the shift from linear RAG pipelines to dynamic, autonomous systems known as Agentic RAG. While traditional RAG follows a fixed Retrieve -> Augment -> Generate sequence, Agentic RAG embeds this process within a reasoning loop run by an autonomous agent. This transforms the system from a simple information processor into an adaptive problem-solver. Agentic RAG systems are built upon a set of core design patterns that enable autonomous behavior :
4.2. Taming the Beast: Context Compression and Filtering in Million-Token Windows As LLMs with context windows of one million tokens or more become commonplace, a new set of challenges has emerged. While vast context windows are powerful, they introduce significant issues with cost, latency, and the "needle-in-a-haystack" problem, where models struggle to identify and use relevant information buried within a sea of irrelevant text. Simply stuffing more documents into the prompt is not a viable strategy. The solution lies in intelligent context compression and filtering. The state-of-the-art in this area has moved beyond simple summarization to more sophisticated, query-aware techniques. A leading example is the Sentinel framework, proposed in May 2025. Sentinel offers a lightweight yet highly effective method for compressing retrieved context before it is passed to the main LLM. The core mechanism of Sentinel is both clever and efficient :
The key advantage of Sentinel is its efficiency and portability. The central finding is that query-context relevance signals are remarkably consistent across different model scales. This means a tiny 0.5B model can act as an effective proxy for a massive 70B model in determining what context is important. On the LongBench benchmark, Sentinel can achieve up to 5x context compression while matching the question-answering performance of systems that use the full, uncompressed context, and it outperforms much larger and more complex compression models. 4.3. Beyond Text: Graph RAG and Structured Knowledge The majority of RAG implementations operate on unstructured text. However, a great deal of high-value enterprise knowledge is structured, residing in databases or knowledge graphs. Graph RAG is an emerging frontier that integrates these structured knowledge sources into the retrieval process. Instead of retrieving disconnected chunks of text, Graph RAG traverses a knowledge graph to retrieve interconnected entities and their relationships. This allows the system to perform complex, multi-hop reasoning that would be nearly impossible with text-based retrieval alone. For example, to answer "Which customers in Germany are using a product that relies on a component from a supplier who recently had a security breach?", a Graph RAG system could traverse the graph from the breached supplier to the affected components, to the products using those components, and finally to the customers who have purchased those products in Germany. This approach enriches the context provided to the LLM with a structured understanding of how different pieces of information relate to one another, unlocking a more profound level of reasoning and analysis. 5. Practical Implementation and Performance Bringing these advanced concepts into production requires a focus on real-world applications, robust measurement, and disciplined engineering practices. This final section of the technical guide provides a pragmatic roadmap for implementing, benchmarking, and maintaining high-performance, context-aware systems. 5.1. Context Engineering in the Wild: Industry Use Cases Context engineering is not a theoretical exercise; it is the driving force behind a new generation of AI applications across numerous industries.
5.2. Measuring What Matters: A Hybrid Benchmarking Framework To build and maintain high-performing systems, one must measure what matters. However, teams often fall into the trap of focusing on a narrow set of metrics. An AI team might obsess over RAG evaluation scores (like faithfulness and relevance) while ignoring a slow and brittle deployment pipeline. Conversely, a platform engineering team might optimize for DORA metrics like cycle time while deploying a model that frequently hallucinates. The performance of a context-aware system is a function of both its AI quality and the engineering velocity that supports it. A truly elite team must track both. This requires a unified "Context Engineering Balanced Scorecard" that bridges the worlds of MLOps and DevOps, providing a holistic view of system health and performance. The logic is straightforward: a model with perfect accuracy is useless if it takes three months to deploy an update. A system with daily deployments is a liability if each deployment introduces new factual errors. Success requires excellence on both fronts. Evaluating complex, multi-component RAG and agentic systems is a critical challenge. A single accuracy score is meaningless. A robust evaluation framework must be multi-faceted, assessing the performance of each component individually and the system as a whole. Component-Level Metrics: Retrieval Quality: The performance of the retriever is the foundation of the entire system. Key metrics include:
Generation Quality: The generator's output must be evaluated against the retrieved context. Key metrics include:
LLM-as-a-Judge: Given the nuance and scale required for evaluation, a popular technique is to use a powerful LLM (like GPT-4o) as an automated evaluator. The "judge" LLM is given the query, the retrieved context, and the generated answer, along with a rubric of criteria (e.g., "Rate the faithfulness of this answer on a scale of 1-5"). This allows for scalable, qualitative assessment. 5.3. Best Practices for Production-Grade Context Pipelines Distilling insights from across the research and practitioner landscape, a set of clear best practices emerges for building robust, production-grade context engineering systems.
6. Failures of Context For a deeper dive into the various failure modes of context understanding, I recommend Drew Breunig's excellent blog in which he highlights 4 diverse challenges of long context -
He also shares potential solutions to effective context management -
7. Resources
Comments
|
Archives
July 2025
Categories
All
Copyright © 2025, Sundeep Teki
All rights reserved. No part of these articles may be reproduced, distributed, or transmitted in any form or by any means, including electronic or mechanical methods, without the prior written permission of the author. Disclaimer
This is a personal blog. Any views or opinions represented in this blog are personal and belong solely to the blog owner and do not represent those of people, institutions or organizations that the owner may or may not be associated with in professional or personal capacity, unless explicitly stated. |
RSS Feed