Your AI assistant sounds smart until someone asks it a question that requires knowing your business. "What did we decide in that meeting last week?" "What is our return policy for damaged items?" "How do I submit an expense report here?" Generic silence. Or worse, confidently wrong answers.
The AI has no idea who is asking, what they have already tried, or what internal knowledge applies. Every question is a blank slate. Every response is a guess.
Meanwhile, the information exists. It sits in your documents, your CRM, your knowledge base, your previous conversations. The AI just never sees it.
Dynamic context assembly gathers the right information from the right sources for each specific request, giving the AI everything it needs to answer correctly.
INTELLIGENCE INFRASTRUCTURE - The mechanism that turns generic AI into AI that actually understands your specific situation.
Dynamic context assembly is the process of gathering relevant information at the moment a request comes in. When someone asks 'What is the status of Project Alpha?', the system pulls the project record, recent updates, related documents, and the person's role on the project. All of this gets assembled into context the AI can use to answer accurately.
The 'dynamic' part is critical. The same question from two different people might require different context. A team lead asking about project status needs financial details and risk flags. A new team member asking the same question needs a high-level summary and who to contact. The assembly adapts based on who, what, when, and why.
Without dynamic assembly, AI is limited to what you manually paste into the prompt. With it, AI automatically gets the context it needs for each unique situation.
Dynamic context assembly solves a universal problem: how do you give an AI system exactly the information it needs for a specific request, without manually gathering it yourself?
Parse the request to identify what is needed. Query relevant data sources. Filter and rank results by relevance. Assemble into a coherent context package. Pass to the AI with the original question.
Select a question, see what context gets assembled, and compare the generic vs. informed response.
Click "Assemble Context" to see what information gets gathered
Search and retrieve
Take the user's question, convert it to search queries, run those queries against your knowledge sources (vector databases, search indices, document stores), and assemble the top results into context. Works well for factual questions where the answer exists somewhere in your data.
Follow the relationships
Identify entities in the request (people, projects, customers, documents) and pull all related information. If someone asks about "the Johnson contract," fetch the contract, the customer record, the sales rep notes, related communications, and current status. Context follows relationships.
Predefined context patterns
For common request types, define exactly what context is needed. A "project status" template always pulls project record, last 5 updates, open blockers, and next milestones. A "customer question" template always pulls customer tier, recent purchases, and open tickets. Consistent assembly for consistent scenarios.
A team member asks a question about internal processes. The system identifies what type of question it is, searches relevant documentation, pulls related policies and procedures, and assembles everything the AI needs to give an accurate, company-specific answer.
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You pull every related document, every historical record, every tangential reference. The AI now has 50,000 tokens of context for a simple question. It takes too long, costs too much, and the AI gets confused by irrelevant information. The answer quality drops because important details are buried in noise.
Instead: Rank by relevance and take only the top results. Set hard limits on context size. Include only what is directly needed for the specific question.
A junior team member and the CEO ask the same question. You serve them identical context. The junior gets overwhelmed with executive-level detail they cannot act on. The CEO gets basic information they already know. Both experiences feel unhelpful.
Instead: Include requester identity in your assembly logic. Adjust depth, detail level, and information type based on role and access level.
You assembled context for a project question yesterday. Today you serve the same cached context. But the project status changed, a blocker was resolved, and a new risk emerged. The AI confidently answers with stale information.
Instead: Cache only truly stable context (company policies, historical records). Re-fetch dynamic data (project status, recent updates, current metrics) on each request.
You have learned how to gather the right information for each request. The natural next step is managing how that context fits within the AI's token limits.