Someone emails your team with a question. The person who responds pulls up the email.
Just the email. No history. No account info. No previous tickets.
They reply based on what they can see. The customer gets frustrated: 'I already explained this three times.'
Meanwhile, the information exists. It's just scattered across five different systems.
The answer was there. Nobody assembled it.
INTERMEDIATE - Requires data from multiple sources to be useful.
Context package assembly is the work that happens before a decision gets made. It pulls together everything relevant from wherever it lives: the customer record, the conversation history, the relevant documentation, the related tickets. You assemble the package so whoever or whatever acts next has the full picture.
Without it, every response starts from scratch. With it, the first response can be the informed response. The difference between 'Let me look into that' and 'Based on your history with us, here is exactly what happened.'
Get it wrong and people waste time hunting for context. Get it right and decisions happen with full information from the start.
Context package assembly solves a universal problem: how do you ensure whoever handles a request has everything they need without asking for information that already exists?
Identify what context is needed for this type of request. Define where each piece lives. Pull it together automatically when a request arrives. Structure it so the handler can actually use it. The result: complete context delivered before anyone has to go looking.
A request just came in. Click each source to include it in the context package. Watch how the possible response improves with more context.
"Let me look into that for you. Can you tell me more about the issue?"
Map the information requirements
For each type of request, define what context makes the response better. A billing question needs payment history and plan details. A technical issue needs system status and recent changes. Be explicit about what helps and what is just noise.
Build the data retrieval layer
Each piece of context lives somewhere. Connect to those sources so you can pull data on demand. The CRM for account info. The ticketing system for history. The knowledge base for documentation. Build the pipes that let you fetch what you need.
Make context actually usable
Raw data dumps do not help. Structure the assembled context so the consumer (human or AI) can actually use it. Summarize long histories. Highlight what changed recently. Put the most relevant information first. Context is only valuable if it is digestible.
This flow turns "let me look into that" into "here is everything you need." When a request arrives, context assembly automatically gathers account info, past interactions, and relevant documentation so the first response can be the informed response.
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You pulled every piece of data connected to this account. 47 pages of context for a simple password reset. The person handling it spent more time wading through context than solving the problem.
Instead: Match context depth to request complexity. Simple requests get minimal context. Complex ones get the full package.
You assembled the context when the request came in. Three hours later, someone responds. In between, the customer called and updated their information. The response used stale context and made things worse.
Instead: Add timestamps to context. For time-sensitive requests, refresh context before acting or flag when it might be stale.
The context package was perfect JSON. Structured, complete, machine-readable. The human looking at it could not find the one piece they needed buried in nested objects.
Instead: Structure context for the actual consumer. If humans read it, make it scannable. If AI processes it, make it parseable. Different consumers need different formats.
You've learned how to gather scattered information into usable context packages. The natural next step is understanding how that context gets used for intelligent routing and response generation.