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Context management

Context management is the deliberate control of resources and information within an execution or interaction context, including their acquisition, organization, retention, use, and cleanup.

How 2 Manning books explain it

Coding with AI

Context management is the deliberate organization of codebase information and prompts so an AI system receives relevant context for producing useful outputs.

How it is used
Improves AI-assisted coding by supplying focused project information for generation, debugging, and multi-file workflows.
How deep it goes
Explains context sources, selection rationale, and prompt specificity, then demonstrates them through a SQLite implementation.
Examples
Uses Cursor context sources and a Blackbox AI Python example that connects to data/questions.db.
What you can do after
Apply it via Cursor or Blackbox AI to formulate focused prompts and integrate generated code after review.

Read how Coding with AI explains context management →

Effective Conversational AI

The ongoing process of tracking, retaining, updating, and applying conversational context as an interaction progresses.

How it is used
Keeps virtual-assistant responses coherent, relevant, and personalized as conversations and user needs change.
How deep it goes
Explains state updates, contextual cues, changing preferences, feedback loops, and RAG integration without implementation-level detail.
Examples
Uses a travel recommendation exchange in which a preference shifts from budget options to luxury accommodations.
What you can do after
Apply dynamic context management to adapt assistant responses, recommendations, and retrieved information as interactions evolve.

Read how Effective Conversational AI explains context management →

Related concepts

These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.