Context engineering is the work of deciding what information an AI model receives for a particular request, and how that information is organized. The practical goal is not to send as much as possible, but to give the model the most useful instructions, facts, and history in a form it can use.
These lessons reflect practitioner guidance from Md Abdul Halim Rafi, co-founder and CTO of AI CRM company Octolane, in an InfoWorld article published November 27, 2025. They are useful design principles, not results from a controlled comparison: the best approach depends on the task and should be measured in your application.
1. Prioritize relevant, recent information over volume
A larger prompt is not automatically a better prompt. Extra material can distract from the current task, compete with important facts, or make it harder to locate the information that matters. Rafi describes unrelated historical email details interfering with deal extraction in his CRM work; that is an author-reported example, not a general benchmark.
For each request, select context based on its connection to the task. A deal-extraction request may need the current opportunity record and recent messages, not every email associated with the contact. A support response may need the current issue and relevant account history, rather than the user’s full interaction archive.
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- Keep instructions and facts needed to answer the current request.
- Exclude history that is merely available but not useful.
- When information is uncertain or incomplete, preserve that uncertainty rather than supplying unrelated material to fill space.
2. Structure context so important details are easy to find
Models receive text, but that does not mean a wall of prose is the clearest way to present it. Headings, labels, delimiters, and structured formats can distinguish instructions from source material and make relevant fields easier to identify.
For example, a profile written as a paragraph can be reorganized into labeled fields such as role, company, preferences, and open_tasks. For records that must be extracted or compared, a consistent schema also makes it clearer which value belongs in which field. Use a format that matches the output and task; structure helps only when it accurately represents the information.
3. Create a hierarchy of context
Not every item in a prompt has equal importance. Put core instructions and the active request in prominent positions, then provide supporting details in an order that helps the model apply them. This reduces the chance that essential directions are buried among examples or background material.
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A practical hierarchy might look like this:
- Core instructions: the task, constraints, and required output format.
- Active query: what the model must answer or do now.
- Relevant facts: retrieved records, recent conversation, or reference material.
- Supporting examples: optional illustrations that clarify the expected result.
The ordering is a design choice, not a guarantee of model behavior. Keep the hierarchy explicit and test whether the model follows the important instructions when supporting material is added.
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Many model requests are processed without the model independently retaining a durable application history between calls. An application can preserve state itself, then select what to send each time. This makes it possible to keep a complete record in the product while giving the model only the relevant portion.
For long conversations, a useful pattern is to retain recent turns verbatim and summarize older exchanges. For documents, retrieve relevant sections rather than repeatedly attaching the entire collection. Rafi describes semantic chunking as splitting material by topic or concept, finding candidate chunks with embeddings, optionally reranking them, and then assembling the selected material into the prompt.
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Techniques for building context efficiently
Retrieve progressively
Start with core instructions and the current query. Add documentation, examples, or other supporting information when the task calls for it. For a large knowledge base, hierarchical retrieval can narrow the search from documents to sections and then to paragraphs, instead of treating every passage as equally relevant.
Compress older or repetitive information
Summarization, entity-and-fact extraction, and structured schemas can reduce repeated or low-value context. Keep information that changes the answer, and avoid compressing away key details such as dates, qualifications, or unresolved uncertainty.
Use conversation windows
Keep a recent window of conversation in full, while representing older turns with a concise summary. Update the summary as the interaction develops, and retain durable facts separately when the application needs them beyond the current exchange.
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Place stable prompt material for caching when supported
If a provider’s caching behavior supports it, put stable prompt content before dynamic query content. This is an implementation option, not a universal benefit: caching behavior and its effect depend on the provider and how the application sends requests.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Measure whether your context strategy works
There is no single context recipe that is best for every workload. Compare alternatives using the outcome you actually need, while accounting for operational trade-offs.
- Response quality: Does the answer correctly complete the target task?
- Retrieval relevance: Do the selected passages contain the evidence needed for the request?
- Context size and token cost: How much input is being sent, and what does it cost under your setup?
- Latency: Does retrieval, reranking, summarization, or additional prompting make responses too slow?
- Implementation complexity: Is the improvement worth the maintenance and system design effort?
- Limit handling: What happens when the selected context exceeds the model’s available limit?
Track context size, cache hits where applicable, retrieval relevance, and task quality together. A smaller prompt is useful only if it preserves or improves the result you need. The InfoWorld article also gives percentage claims for reductions in context size and token costs and for quality improvement, but it does not identify an original study for those figures; they should not be treated as established general statistics.
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Plan explicitly for context overflow
When the selected material will not fit, protect the active query and essential instructions first. Then summarize or trim lower-priority supporting material. Do not silently drop content: record or surface the truncation so the application and user can distinguish a complete answer from one made with incomplete context.
In Rafi’s formulation, “The goal isn’t to maximize context. It’s to provide the right information, in the right format, at the right position.”
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