Table of Contents

What is Context Engineering?


TL;DR

Context engineering is the discipline of designing, selecting, and managing the information that goes into a language model's context window to maximize output quality. While prompt engineering focuses on how you ask the question, context engineering focuses on what information surrounds the question. It encompasses decisions about what to retrieve, what to remember, what to summarize, what to discard, and how to structure all of it within the finite token budget of a context window. As AI applications grow more complex, context engineering has become the primary lever for improving results: the same model produces dramatically different outputs depending on the context it receives.


What Exactly is Context Engineering?

Every time a language model generates a response, it works from a fixed-size context window containing everything the model can "see": the system prompt, conversation history, retrieved documents, tool results, memory entries, and the current user query. This context window is the model's entire world for that generation step. Anything not in the context does not exist from the model's perspective.

Context engineering is the art and science of curating this window to contain exactly the right information:

+--------------------------------------------------+
|                Context Window                    |
|  (finite: 4K, 8K, 32K, 128K tokens)              |
|                                                  |
|  +------------------+  +---------------------+   |
|  | System Prompt    |  | Retrieved Documents |   |
|  | (instructions,   |  | (RAG results,       |   |
|  |  persona, rules) |  |  web search hits)   |   |
|  +------------------+  +---------------------+   |
|                                                  |
|  +------------------+  +---------------------+   |
|  | Conversation     |  | Memory Entries      |   |
|  | History          |  | (relevant facts,    |   |
|  | (recent turns)   |  |  user preferences)  |   |
|  +------------------+  +---------------------+   |
|                                                  |
|  +------------------+  +---------------------+   |
|  | Tool Results     |  | Current Query       |   |
|  | (API responses,  |  | (what the user is   |   |
|  |  file contents)  |  |  asking right now)  |   |
|  +------------------+  +---------------------+   |
+--------------------------------------------------+

The challenge is that not everything fits. A 128K-token context window sounds large, but a few PDF documents, a conversation history, and some tool results can fill it quickly. Worse, stuffing the context with too much information degrades quality: models pay less attention to each piece of information when the context is noisy or bloated.

Context engineering means making intelligent tradeoffs about what earns a place in the window.

Context Engineering vs. Prompt Engineering

These disciplines are complementary but distinct:

Aspect Prompt Engineering Context Engineering
Focus How you phrase the instruction What information surrounds the instruction
Scope The user query and system prompt The entire context window
Techniques Instruction phrasing, few-shot examples, chain-of-thought Retrieval selection, memory management, context recycling, overflow policies
Analogy Asking the right question Giving the right briefing materials
Impact Moderate (guides reasoning) High (determines what the model knows)

A perfectly engineered prompt fails if the context lacks the information needed to answer. A poorly phrased prompt can still succeed if the context contains highly relevant, well-organized information.


Why Context Engineering Matters

  1. Quality is Context-Dependent: The same model with the same prompt produces dramatically different results depending on the context. A GPT-class model answering "What is our refund policy?" is useless without the actual policy document in context, and brilliant with it.

  2. Context Windows Are Finite: Even the largest context windows have limits. When your knowledge base has millions of documents, you cannot include everything. Choosing the right 10 documents out of 10,000 is a context engineering problem.

  3. More Context is Not Always Better: Research consistently shows that models lose track of information in the middle of long contexts (the "lost in the middle" phenomenon). A shorter, more focused context often produces better results than a longer, noisier one.

  4. Cost Scales with Context: Longer contexts mean more tokens processed, which means higher inference costs and latency. Efficient context engineering reduces both.

  5. Multi-Turn Conversations Accumulate Context: As conversations grow, the history consumes an increasing share of the context window. Without management (summarization, recycling, selective retention), the model eventually runs out of room for new information.

  6. Agent Systems Compound the Problem: AI agents that use tools, memory, and retrieval generate enormous amounts of intermediate context. Each tool call result, each retrieved document, each memory entry competes for space in the context window.


Technical Insights

The Five Pillars of Context Engineering

1. Retrieval: Getting the Right Information In

The most impactful context engineering decision is what external information to include. This is primarily the domain of RAG (Retrieval-Augmented Generation):

  • Embedding quality: Better embeddings produce more relevant retrievals
  • Chunk size: Chunking strategy determines the granularity of retrieval. Too large, and you waste context on irrelevant surrounding text. Too small, and you lose necessary context. See Optimize RAG with Custom Chunking.
  • Top-K selection: How many chunks to include. More is not always better.
  • Reranking: A second-pass model that rescores retrieved chunks for relevance, pushing the most useful ones to the top.
  • Source diversity: Retrieving from multiple sources avoids single-source bias.

In agentic RAG, the agent actively controls retrieval decisions, reformulating queries and adjusting strategy based on result quality.

2. Memory Management: Retaining What Matters

Agent memory persists knowledge across sessions, but not all memories are equally relevant at any given moment:

  • Relevance-based retrieval: Only inject memory entries relevant to the current query, not the entire memory store.
  • Time-decay scoring: Recent memories may be more relevant than old ones. LM-Kit.NET's AgentMemory supports time-decay policies.
  • Capacity limits and eviction: Set maximum memory size and evict low-relevance entries automatically.
  • Memory consolidation: Merge redundant or overlapping memories into concise summaries. See the Use Agent Memory guide.

3. Conversation Management: Handling Growing Histories

Multi-turn conversations grow with each exchange. Without management, the conversation history eventually fills the entire context window:

4. Prompt Structure: Organizing Within the Window

How information is arranged within the context window affects how well the model uses it:

  • System prompt placement: Instructions and persona at the start of the context.
  • Retrieved context positioning: Place the most relevant information close to the query (end of context), not buried in the middle.
  • Clear section boundaries: Use delimiters or headers to separate different types of information (instructions, context, history, query).
  • Prompt templates: Use structured templates with conditionals, loops, and helpers to dynamically compose the prompt based on available context. See Build Dynamic Prompts with Templates.

5. Information Compression: Fitting More in Less Space

When there is more relevant information than context window space:

  • Summarization: Condense long documents into shorter summaries that preserve key information.
  • Extraction over inclusion: Instead of including an entire document, extract only the relevant sections or facts.
  • Token efficiency: Some representations are more token-efficient than others. Structured data (JSON, tables) can be more compact than prose for the same information.
  • Chunking strategy: Intelligent chunking ensures retrieved fragments are self-contained and information-dense.

The Context Engineering Loop

For AI agents, context engineering is not a one-time setup. It is a continuous process during execution:

1. Receive user query
   |
2. Assess: What information does the model need to answer this?
   |
3. Retrieve: Fetch relevant documents, memories, tool results
   |
4. Select: Choose the most relevant pieces (rerank, filter, deduplicate)
   |
5. Compress: Summarize or extract if total exceeds budget
   |
6. Arrange: Structure the context window optimally
   |
7. Generate: Model produces response from curated context
   |
8. Update: Store new knowledge in memory, update conversation history
   |
   +---> Back to step 1 for next turn

Context Budget Planning

A practical framework for allocating the context window:

Total Context Window: 32,768 tokens (example)

Allocation:
  System Prompt:        ~500 tokens  ( 1.5%)  Fixed instructions
  Tool Definitions:   ~2,000 tokens  ( 6.1%)  Available tools schema
  Memory Entries:     ~2,000 tokens  ( 6.1%)  Relevant long-term knowledge
  Retrieved Context:  ~8,000 tokens  (24.4%)  RAG results
  Conversation History:~12,000 tokens (36.6%)  Recent turns
  Current Query:        ~500 tokens  ( 1.5%)  User's question
  Generation Budget:  ~7,768 tokens  (23.7%)  Room for the response

Each application needs a different allocation. A research assistant might allocate 50% to retrieved context. A conversational chatbot might allocate 50% to conversation history. Context engineering is about making these tradeoffs explicit and intentional.


Practical Use Cases

  • Enterprise Knowledge Assistants: Select the most relevant documents from a large knowledge base, rerank for precision, and include only the top results. The difference between retrieving 3 highly relevant paragraphs and 20 loosely related ones can be the difference between a perfect answer and a hallucinated one.

  • Long-Running Agent Sessions: An agent conducting multi-step research accumulates tool results, retrieved documents, and reasoning traces. Context engineering keeps the window focused on the current subtask while retaining critical earlier findings.

  • Multi-User Assistants: Different users have different preferences and histories. Context engineering selects the right user-specific memories and preferences to include. LM-Kit.NET's UserScopedMemory enables per-user context personalization.

  • Document Q&A over Large Corpora: When the relevant answer might be anywhere in a 500-page document, chunking strategy, embedding quality, and retrieval precision determine whether the right paragraph makes it into the context.

  • Conversational Commerce: A shopping assistant must maintain the user's preferences, cart state, and conversation history while also retrieving product information. Context budget allocation is critical.


Key Terms

  • Context Engineering: The discipline of selecting, organizing, and managing the information within a language model's context window to maximize output quality.

  • Context Window: The fixed-size input buffer that contains everything the model can process for a given generation step. See Context Windows.

  • Context Budget: The allocation plan for how the context window's token capacity is distributed across different information types.

  • Lost in the Middle: The empirical finding that language models pay less attention to information in the middle of long contexts compared to the beginning and end.

  • Context Recycling: Summarizing or compressing older context to free up space for new information while retaining essential knowledge.

  • Overflow Policy: The strategy applied when input exceeds the context window capacity: truncation, summarization, or error.

  • Retrieval Precision: The accuracy of selecting relevant information from a large corpus. Higher precision means less noise in the context.

  • Information Density: The ratio of useful information to total tokens in the context. Higher density yields better results.





External Resources


Summary

Context engineering is the discipline that determines whether an AI application succeeds or fails in production. While model capability sets the ceiling, context quality determines where on that spectrum the application actually performs. By carefully selecting what retrieved documents to include, which memories to surface, how to manage growing conversation histories, and how to structure all of it within a finite token budget, context engineering maximizes the value extracted from every model inference. It is the bridge between a capable model and a capable application, and its importance only grows as AI systems become more complex, incorporating agents, tools, orchestrators, and multi-step workflows that all compete for space in the context window.

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