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, I saw our production system fail spectacularly. Not a code bug, not an infrastructure error, but simply misunderstanding the optimization goals of our AI system. We built what we thought was a fancy document analysis pipeline with retrieval-augmented generation (RAG), vector embeddings, semantic search, and fine-tuned reranking. When we demonstrated the system, it answered questions about our client’s regulatory documents very convincingly. But in production, the system answered questions completely context free.
The revelation hit me during a post-mortem meeting: we weren’t managing information retrieval but we were managing context distribution. And we were terrible at it.
This failure taught me something that’s become increasingly clear across the AI industry: context isn’t just another input parameter to optimize. Rather, it is the central currency that defines whether an AI system delivers real value or remains a costly sideshow. Unlike traditional software engineering, in which we optimize for speed, memory, or throughput, context engineering requires us to regard information as humans do: layered, interdependent, and reliant on situational awareness.
The Context Crisis in Modern AI Systems
Before we look into potential solutions, it is crucial to identify why context has become such a critical choke point. It is not an issue from a technical point of view. It is more of a design and philosophical issue.
Most AI implemented today takes into account context as a fixed-sized buffer which is filled with pertinent information ahead of processing. This worked well enough with the early implementations of chatbots and question-answering systems. However, with the increasing sophistication of AI applications and their incorporation into workflows, the buffer-based methodology has proved to be deeply insufficient.
Let’s take a typical enterprise RAG system as an example. What happens when a user inputs a question? The system performs the following actions:
- Converts the question into embeddings
- Searches a vector database for similar content
- Retrieves the top-k most similar documents
- Stuffs everything into the context window
- Generates an answer
This flow is based on the hypothesis that clustering embeddings in some space of similarity can be treated as contextual reason which in practice fails not just occasionally, but persistently.
The more fundamental flaw is the view of context as static. In a human conversation, context is flexible and shifts within the course of a dialogue, moving and evolving as you progress through a conversation, a workflow. For example, if you were to ask a colleague “the Johnson report,” that search does not just pulse through their memory for documents with those terms. It is relevant to what you are working on and what project.
From Retrieval to Context Orchestration
The shift from thinking about retrieval to thinking about context orchestration represents a fundamental change in how we architect AI systems. Instead of asking “What information is most similar to this query?” we need to ask “What combination of information, delivered in what sequence, will enable the most effective decision-making?”
This distinction matters because context isn’t additive, rather it’s compositional. Throwing more documents into a context window doesn’t improve performance in a linear fashion. In many cases, it actually degrades performance due to what some researchers call “attention dilution.” The model’s attention focus spreads too thin and as a result, the focus on important details weakens.
This is something I experienced firsthand when developing a document analysis system. Our earliest versions would fetch every applicable case, statute, and even regulation for every single query. While the results would cover every possible angle, they were absolutely devoid of utility. Picture a decision-making scenario where a person is overwhelmed by a flood of relevant information being read out to them.
The moment of insight occurred when we began to think of context as a narrative structure instead of a mere information dump. Legal reasoning works in a systematic way: articulate the facts, determine the applicable legal principles, apply them to the facts, and anticipate counterarguments.
| Aspect | RAG | Context Engineering |
| Focus | Retrieval + Generation | Full lifecycle: Retrieve, Process, Manage |
| Memory Handling | Stateless | Hierarchical (short/long-term) |
| Tool Integration | Basic (optional) | Native (TIR, agents) |
| Scalability | Good for Q&A | Excellent for agents, multi-turn |
| Common Tools | FAISS, Pinecone | LangGraph, MemGPT, GraphRAG |
| Example Use Case | Document search | Autonomous coding assistant |
The Architecture of Context Engineering
Effective context engineering requires us to think about three distinct but interconnected layers: information selection, information organization, and context evolution.
Information Selection: Beyond Semantic Similarity
The first layer focuses on developing more advanced methods on how to define what the context entails. Traditional RAG systems place far too much emphasis on embedding similarity. This approach overlooks key elements of the missing, how the missing information contributes to the understanding.
It is my experience that the most useful selection strategies incorporate many different unders.
Relevance cascading begins with more general broad semantic similarity, and then focuses on more specific filters. To illustrate, in the regulatory compliance system, first, there is a selection of semantically relevant documents, then documents from the relevant regulatory jurisdiction are filtered, followed by prioritizing documents from the most recent regulatory period, and finally, ranking by recent citation frequency.
Temporal context weighting recognizes that the relevance of information changes over time. A regulation from five years ago might be semantically linked to contemporary issues. However, if the regulation is outdated, then incorporating it into the context would be contextually inaccurate. We can enforce decay functions that automatically downweight outdated information unless explicitly tagged as foundational or precedential.
User context integration goes beyond the immediate query to consider the user’s role, current projects, and historical interaction patterns. When a compliance officer asks about data retention requirements, the system should prioritize different information than when a software engineer asks the same question, even if the semantic content is identical.
Information Organization: The Grammar of Context
Once we have extracted the relevant information, how we represent it in the context window is important. This is the area where typical RAG systems can fall short – they consider the context window as an unstructured bucket rather a thoughtful collection of narrative.
In the case of organizing context that is effective, the framework should also require that one understands the process known to cognitive scientists as “information chunking.” Human working memory can maintain approximately seven discrete pieces of information at once. Once going beyond it our understanding falls precipitously. The same is true for AI systems not because their cognitive shortcomings are identical, but because their training forces them to imitate human like reasoning.
In practice, this means developing context templates that mirror how experts in a domain naturally organize information. For financial analysis, this might mean starting with market context, then moving to company-specific information, then to the specific metric or event being analyzed. For medical diagnosis, it might mean patient history, followed by current symptoms, followed by relevant medical literature.
But here’s where it gets interesting: the optimal organization pattern isn’t fixed. It should adapt based on the complexity and type of query. Simple factual questions can handle more loosely organized context, while complex analytical tasks require more structured information hierarchies.
Context Evolution: Making AI Systems Conversational
The third layer context evolution is the most challenging but also the most important one. The majority of existing systems consider each interaction to be independent; therefore, they recreate the context from zero for each query. Yet providing effective human communication requires preserving and evolving shared context as part of a conversation or workflow.
But architecture that evolves the context in which the AI system runs will be another matter; what gets shifted is how to manage its state in one kind of space of possibilities. We’re not simply maintaining data state we’re also maintaining understanding state.
This “context memory” — a structured representation of what the system has figured out in past interactions — became part of our Document Response system. The system doesn’t treat the new query as if it exists in isolation when a user asks a follow-up question.
It considers how the new query relates to the previously established context, what assumptions can be carried forward, and what new information needs to be integrated.
This approach has profound implications for user experience. Instead of having to re-establish context with every interaction, users can build on previous conversations, ask follow-up questions that assume shared understanding, and engage in the kind of iterative exploration that characterizes effective human-AI collaboration.
The Economics of Context: Why Efficiency Matters
The cost of reading context is proportional to computational power, and it might soon become cost-prohibitive to maintain complex AI applications that are ineffective in reading context.
Do the math: If your context window involves 8,000 tokens, and you have some 1,000 queries per day, you are eating up 8 million tokens per day for context only. At present pricing systems, the cost of context inefficiency can easily dwarf the cost of the task generation itself.
But the economics extend beyond the direct costs of computation. A bad context management directly causes slower response time and thus worse user experience and less system usage. It also increases the probability of repeating errors, which has downstream costs in user’s confidence and manual patches created to fix issues.
The most successful AI implementations I’ve observed treat context as a constrained resource that requires careful optimization. They implement context budgeting—explicit allocation of context space to different types of information based on query characteristics. They use context compression techniques to maximize information density. And they implement context caching strategies to avoid recomputing frequently used information.
Measuring Context Effectiveness
One of the challenges in context engineering is developing metrics that actually correlate with system effectiveness. Traditional information retrieval metrics like precision and recall are necessary but not sufficient. They measure whether we’re retrieving relevant information, but they don’t measure whether we’re providing useful context.
In our implementations, we’ve found that the most predictive metrics are often behavioral rather than accuracy-based. Context effectiveness correlates strongly with user engagement patterns: how often users ask follow-up questions, how frequently they act on system recommendations, and how often they return to use the system for similar tasks.
We’ve also implemented what we call “context efficiency metrics”; it measures of how much value we’re extracting per token of context consumed. High-performing context strategies consistently provide actionable insights with minimal information overhead.
Perhaps most importantly, we measure context evolution effectiveness by tracking how system performance improves within conversational sessions. Effective context engineering should lead to better answers as conversations progress, as the system builds more sophisticated understanding of user needs and situational requirements.
The Tools and Techniques of Context Engineering
Developing effective context engineering requires both new tools and also new ways to think about old tools. New tools are developed and available every month, but the strategies that ultimately work in production seem to match familiar patterns:
Context routers make decisions dynamically based on identifying query elements. Instead of fixed retrieval strategies, they assess components of the query like f intent, effort complexity, and situational considerations. This is to devise strategies based on some form of optimization to select and organize information.
Context compressors borrow from information theory and create what I think of as max logic to incorporate maximally impute density factor within a context window. These are not merely text summarisation tools, these are systems that attend to storing the most contextually rich information and reduce noise as well as redundancy.
Context state managers develop structured representations about conversational state and workflow state – so that AI systems learn, rather than are born anew with each different intervention or component of interaction.
Context engineering requires thinking about AI systems as partners in ongoing conversations rather than oracle systems that respond to isolated queries. This changes how we design interfaces, how we structure data, and how we measure success.
Looking Forward: Context as Competitive Advantage
As AI functionality becomes more standardized, context engineering is becoming our differentiator.
AI applications may not employ more advanced model architectures or more complex algorithms. Rather, they enhance existing capabilities further for greater value and reliability through better context engineering.
The implications run deeper than the specific environment in which implementations take place, to one’s organizational strategy. Companies that focus on context engineering as a core competency as part of their differentiated organizational strategy, will outperform competitors who simply emphasize their model capabilities and not their information architectures, user workflows and domain-specific reasoning patterns.
A new survey analyzing over 1,400 AI papers has found something quite interesting: we’ve been thinking about AI context completely wrong. While everyone’s been obsessing over bigger models and longer context windows, researchers discovered that our AIs are already amazing at understanding complex information, they just suck at using it properly. The real bottleneck isn’t model intelligence; it’s how we feed information to these systems.
Conclusion
The failure that started this exploration taught me that building effective AI systems isn’t primarily about having the best models or the most sophisticated algorithms. It’s about understanding and engineering the flow of information in ways that enable effective decision-making.
Context engineering is becoming the differentiator for AI systems that provide real value, versus those that remain interesting demos.
The future of AI is not creating systems that understand everything, it is creating systems that accurately understand what the system should pay attention to, when to pay attention, and how that attention can be converted to action and insight.
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