# Context assembly: building the prompt the model actually sees

DevFeed: [Context assembly: building the prompt the model actually sees](<https://devfeed.tech/articles/context-assembly-building-the-prompt-the-model-actually-sees-4781.md>)

Original publisher: [Read original article](<https://redis.io/blog/context-assembly-building-the-prompt-the-model-sees/>)

Author: Jeff Mills

Published: 2026-07-22T00:00:00Z

Content type: tutorial

Language: en

Sources: [Redis Blog](<https://devfeed.tech/sources/redis-blog.md>)

Topics: [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Retrieval Augmented Generation (RAG)](<https://devfeed.tech/topics/retrieval-augmented-generation-rag.md>), [Prompt Engineering](<https://devfeed.tech/topics/prompt-engineering.md>), [long-context](<https://devfeed.tech/topics/long-context.md>), [AI search](<https://devfeed.tech/topics/ai-search.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [Database](<https://devfeed.tech/topics/database.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [context-window](<https://devfeed.tech/tags/context-window.md>), [database](<https://devfeed.tech/tags/database.md>), [inference](<https://devfeed.tech/tags/inference.md>), [llm](<https://devfeed.tech/tags/llm.md>), [model](<https://devfeed.tech/tags/model.md>), [prompt-engineering](<https://devfeed.tech/tags/prompt-engineering.md>), [rag](<https://devfeed.tech/tags/rag.md>), [retrieval](<https://devfeed.tech/tags/retrieval.md>), [tech-de](<https://devfeed.tech/tags/tech-de.md>), [tokens](<https://devfeed.tech/tags/tokens.md>), [tools](<https://devfeed.tech/tags/tools.md>), [vector](<https://devfeed.tech/tags/vector.md>)

## AI overview

A guide to context assembly, the process of combining system instructions, retrieved documents, conversation history, tool schemas, and memories into the token sequence an LLM receives. It explains how context engineering differs from prompt engineering and how retrieval, memory, ordering, and token budgets affect model responses.

## Source excerpt

The prompt a production LLM receives is almost never something a person wrote. By the time a request reaches the model, your app has stitched together system instructions, retrieved documents, conversation history, tool schemas, and stored memories in...