Context Engineering includes five types: context compression for reducing size while preserving meaning, context window management for prioritizing what deserves limited space, dynamic context assembly for gathering the right context per request, memory architectures for AI persistence across sessions, and token budgeting for allocating tokens across prompt sections. The right choice depends on where AI is struggling. Most systems need multiple types working together. Compression and window management handle the input. Assembly and memory handle the sources. Budgeting coordinates everything.
You fed the AI your 47-page operations manual, the full customer history, and every document you could find.
The AI ignores the most important details, hallucinates answers you explicitly covered, and cuts off mid-sentence.
More information was supposed to make it smarter. Instead, it made everything worse.
What you include matters less than what you prioritize.
Part of Layer 2: Intelligence Infrastructure - Where AI systems become usable.
Context Engineering is the discipline of deciding what information goes into an AI prompt, how it is organized, and what gets remembered across interactions. Without it, AI systems are either starved of context or drowning in noise. With it, a fraction of the information produces dramatically better results.
These components work together. Compression reduces size. Window management prioritizes importance. Assembly gathers the right pieces. Memory persists what matters. Budgeting allocates the limited space. Each solves a different part of the context problem.
Each component solves a different context problem. The right choice depends on where your AI is struggling.
Compression | Window Mgmt | Assembly | Memory | Budgeting | |
|---|---|---|---|---|---|
| What It Solves | Continuity across interactions | ||||
| When It Runs | Between and during sessions | ||||
| Key Question | What should persist? | ||||
| Primary Tradeoff | Persistence vs cleanup |
The right choice depends on where your AI is struggling. Answer these questions to find your starting point.
“AI ignores important details buried in long documents”
Compression reduces size while preserving what matters, so critical details surface.
“Responses get truncated or the AI misses key instructions”
Window management ensures the most important content gets processed first.
“AI gives generic answers that ignore your specific business context”
Assembly gathers the right context from your systems for each unique request.
“AI forgets what you discussed in previous conversations”
Memory architectures give AI persistence across sessions and interactions.
“Sometimes great responses, sometimes incomplete or wandering ones”
Budgeting ensures consistent allocation across system prompt, context, and output.
Answer a few questions to get a recommendation.
Context engineering is not about AI. It is about fitting the right information into limited capacity. The same discipline applies anywhere you face information overload.
More information is available than can be processed at once
Compress, prioritize, assemble, and allocate based on the task at hand
Better decisions from less noise
When pulling a monthly report requires reviewing 50 pages of data...
That's a compression problem - distill the 50 pages into the 5 metrics that matter.
When new hires cannot absorb everything in their first week...
That's a window management problem - sequence what they need first, defer the rest.
When someone forwards a 47-email thread and says "thoughts?"...
That's a context assembly problem - extract the key decisions and current blockers.
When the same context gets re-explained in every meeting...
That's a memory problem - persist decisions so they do not need repeating.
Which of these sounds most like your current situation?
These mistakes seem small at first. They compound into hallucinations, missed details, and wasted tokens.
Move fast. Structure data “good enough.” Scale up. Data becomes messy. Painful migration later. The fix is simple: think about access patterns upfront. It takes an hour now. It saves weeks later.
Context engineering is the discipline of controlling what information goes into an AI prompt, how it is organized, and what gets remembered across interactions. It includes five components: compression to reduce size, window management to prioritize content, assembly to gather relevant context, memory to persist across sessions, and budgeting to allocate tokens. Without context engineering, AI systems are either starved of context or drowning in noise.
AI models have limited attention capacity. When you dump everything into the prompt, important details compete with irrelevant ones. The model spreads its attention thin across all content, often focusing on tangentially related information instead of the most relevant. Position bias makes this worse: models pay more attention to beginning and end, potentially missing critical information buried in the middle.
Context compression reduces the SIZE of information, making long documents shorter while preserving meaning. Context window management controls the ORDER and PRIORITY, deciding what deserves limited space and ensuring critical content gets processed first. You typically use compression on retrieved content, then window management to organize the compressed content in the prompt.
Use dynamic context assembly when your AI needs different information for different requests. Instead of static context, the system gathers relevant documents, records, and data at request time based on who is asking, what they are asking, and what entities are involved. This turns generic AI into AI that understands your specific business context.
Conversation history is just a log of messages. Memory architectures are structured systems for deciding what to remember, for how long, and when to recall it. They include working memory (current task), short-term memory (recent interactions), and long-term memory (persistent facts). Without architecture, memory either grows forever or forgets what matters.
Token budgeting is allocating your available tokens across competing demands: system instructions, few-shot examples, retrieved context, and output space. Without budgets, one category can crowd out others. You might use all tokens on context, leaving no room for the response. Budgeting ensures predictable, consistent prompt composition.
The biggest mistakes are: overwhelming AI with too much context (quality drops, not improves), putting critical instructions at the end (they get truncated), treating all context as equally important (trivia crowds out essentials), memory that grows forever (outdated information surfaces), and not reserving output space (responses get cut off).
They form a pipeline. Token budgeting sets the overall allocation. Dynamic assembly gathers relevant content. Context compression reduces size. Window management prioritizes order. Memory provides persistence across sessions. A complete system uses all five, but you can add them incrementally based on where your AI is struggling most.
Start with token budgeting to establish your foundation: how many tokens for system prompt, examples, context, and output. Then add window management to ensure critical content gets priority. Add compression when documents are too long. Add assembly when you need business-specific context. Add memory when you need persistence.
Context engineering sits in Layer 2 (Intelligence Infrastructure). It depends on retrieval architecture (chunking, search, embeddings) from earlier in Layer 2. It feeds into Layer 3 (Understanding & Analysis) for context package assembly. The quality of context engineering directly determines the quality of AI generation downstream.
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