Reference engineering explained in 3 levels of difficulty

Reference engineering explained in 3 levels of difficulty. Image by author

, Introduction

Large language model (LLM) applications constantly affect context window boundaries. The model forgets earlier instructions, loses track of relevant information, or degrades in quality as interactions progress. This is because the LLM has set token budget, but the applications generate unlimited information – conversation history, retrieved documents, file uploads, application programming interface (API) responses and user data. Without management, important information gets haphazardly cut off or never enters the context.

Context engineering treats the context window as a managed resource. With clear allocation policies and memory systems. You decide what information enters the context, when it enters, how long it lasts, and what is compressed or stored in external memory for retrieval. It organizes the information flow at runtime of the application rather than expecting everything to go well or accepting poor performance.

This article explains context engineering at three levels:

1. Understanding the Basic Requirements of Reference Engineering
2. Applying practical optimization strategies in production systems
3. Reviewing advanced memory architectures, retrieval systems, and optimization techniques

The following sections explore these levels in detail.

, Level 1: Understanding the Context Constraints

LLMs have fixed reference windows. Whatever the model knows at the time of inference must fit into those tokens. This isn’t a big problem with single-turn completions. For retrieval-augmented generation (RAG) applications and AI agents running multi-step tasks with tool calls, file uploads, conversation histories, and external data, this creates an optimization problem: What information gets attention and what is discarded?

Let’s say you have an agent that runs multiple steps, makes 50 API calls and processes 10 documents. Such agentic AI systems will likely fail without explicit context management. The model forgets important information, hallucinates tool output, or degrades in quality as the interaction progresses.

Context Engineering Level 1 | Image by author

Context engineering is about designing Continuous accumulation of the information environment around the LLM during its execution.This involves managing what enters the context, when, for how long, and what is removed when space is exhausted,

, Level 2: Adapting to context in practice

Effective context engineering requires clear strategies across multiple dimensions.

, budgeting token

Allocate your context window intentionally. System instructions may take 2K tokens. Conversation history, tool schema, recovered documents, and real-time data can all be quickly connected. With a very large reference window, there is ample scope. With a very short window, you are forced to make difficult compromises about what to keep and what to discard.

, cutting the conversation short

Keep recent turns, skip middle turns, and preserve important early context. Summarization works but loses credibility. Some systems apply semantic compression – Extracting key facts rather than preserving text verbatim. Test where your agent breaks down as the conversation progresses.

, Managing tool output

Larger API responses consume tokens faster. Request specific fields instead of the full payload, truncate results, summarize before returning to the model, or use multi-pass strategies where the agent first fetches metadata and then requests details only for relevant objects.

, Using Model Reference Protocol and On-Demand Retrieval

Instead of preloading everything, connect the model to external data sources to use it when needed Model Reference Protocol (MCP). Based on task requirements the agent decides what to bring. This changes the problem from “fitting everything into context” to “getting the right things in at the right time.”

, separating structured states

Insert static instructions into system messages. Insert mutable data into user messages where it can be updated or deleted without touching the main directives. Treat interaction history, tool output, and recovered documents as separate streams with independent management policies.

Context Engineering Level 2 | Image by author

The practical variation here is to treat the context as a Dynamic resources that require active management at runtime of the agentNot a static thing that you configure once.

, Level 3: Applying reference engineering in production

Large-scale reference engineering requires sophisticated memory architectures, compression strategies, and retrieval systems working together. Here’s how to build a production-grade implementation.

, Designing Memory Architecture Patterns

apart Memory in agentic AI systems In levels:

• Working memory (active context window)
• Episodic memory (compressed conversation history and task status)
• Semantic memory (facts, documents, knowledge base)
• procedural memory (instructions)

Working memory is what the model sees now, which has to be adapted to immediate task requirements. Episodic memory stores what happened. You can apply pressure aggressively but preserve temporary relationships and causal chains. For semantic memory, store indexes based on topic, entity, and relevance for faster retrieval.

, Applying Compression Techniques

Simple summarization misses important details. A better approach is extractive compression, where you identify and preserve high-information-density sentences while removing filler.

• For tool output, extract structured data (entities, metrics, relationships) rather than prose summaries.
• For conversations, accurately preserve user intentions and agent commitments while compressing logic chains.

, designing a recovery system

When the model needs information rather than context, retrieval quality determines success. Apply hybrid search: dense embeddings for semantic similarity, BM25 for keyword matchingAnd metadata filters for precision.

Rank results based on recency, relevance, and information density. return to top K But also near-misses on the surface; The model must know what approximately matches. Retrieval occurs in context, so the model sees the query formulation and results. Bad questions produce bad results; Uncover it to enable self-improvement.

, Token level optimization

Continuously profile your token usage.

• System instructions consuming 5K tokens that could be 1K? Rewrite them.
• Device schema verbose? use compact JSON Schema instead of full OpenAPI Specification.
• Does the conversation repeat similar content? Duplicate.
• Are the retrieved documents overlapping? Merge before adding to context.

Each token saved is a token available for task-critical information.

, Triggering memory retrieval

The model should not be constantly retrieved; This is expensive and adds latency. Apply smart triggers: Retrieve when the model explicitly requests information, when it detects knowledge gaps, when tasks switch, or when the user references a previous context.

When retrieval does not yield anything useful, the model should know this clearly rather than hallucinating. Return empty results with metadata: “No documents found matching the query X in the encyclopedia Y.” This lets the model adjust the strategy by rephrasing the query, searching for a different source, or notifying the user that the information is not available.

Context Engineering Level 3 | Image by author

, Synthesis of multi-document information

When an argument requires multiple sources, process hierarchically.

• First pass: Extract key facts from each document independently (parallel).
• Second pass: Load the extracted facts into context and synthesize.

This avoids context exhaustion from loading 10 full documents while preserving multi-source reasoning capability. For contradictory sources, preserve the contradiction. Let the model see conflicting information and resolve it or flag it to draw the user’s attention.

, continuous conversation status

For agents that stop and resume, serialize the context state to external storage. Save compressed conversation history, current task graph, tool output and recovery cache. On the resume, reconstruct the minimum necessary context; Don’t reload everything.

, Performance evaluation and measurement

Track key metrics to understand how your context engineering strategy is performing. Monitor context usage to see the average percentage of the window being used, and eviction frequency to understand how often you are exceeding context limits. Measure retrieval accuracy by checking what fraction of retrieved documents is actually relevant and used. Finally, track the persistence of information to see how many important facts survive before being lost.

, wrapping up

Reference engineering is ultimately about information architecture. You are building a system where the model has access to everything in its context window and no access to what is not. Every design decision – what to compress, what to retrieve, what to cache, and what to discard – creates the information environment in which your application operates.

If you don’t focus on context engineering, your system may hallucinate, forget important details, or degrade over time. Get it right and you’ll get an LLM application that remains consistent, reliable, and effective across complex, extended interactions despite inherent architectural limitations.

Happy context engineering!

, References and further learning

Bala Priya C is a developer and technical writer from India. She likes to work in the fields of mathematics, programming, data science, and content creation. His areas of interest and expertise include DevOps, Data Science, and Natural Language Processing. She loves reading, writing, coding, and coffee! Currently, she is working on learning and sharing her knowledge with the developer community by writing tutorials, how-to guides, opinion pieces, and more. Bala also creates engaging resource overviews and coding tutorials.