lAItest / AI field guide · edition 2026-09-26
Know the language. Build with clarity.
Plain-English definitions for the concepts, patterns, protocols, and tools behind modern AI work.
414 defined terms · 16 topic areas · 50 essentials
Context & memory terms
29 matching terms.
- Compaction Concept · Context & memory
Replace detailed history with a smaller representation that preserves task-relevant information.
- Context engineering Concept · Context & memory
Select, structure, and update the information available to the model throughout execution.
- Progressive disclosure Concept · Context & memory
Expose concise metadata first and fuller instructions or resources only when relevant.
- Context budget Concept · Context & memory
The allocation of limited context capacity across instructions, history, tools, evidence, and output.
- Context poisoning Concept · Context & memory
Introduce misleading or malicious information into the material a model uses.
- Context pruning Concept · Context & memory
Remove information that is no longer useful for the current task.
- Context reset Concept · Context & memory
Start a fresh context while explicitly carrying forward selected persistent state.
- Context rot Concept · Context & memory
An informal label for reduced effectiveness as context becomes long, noisy, or distracting.
- Context window Concept · Context & memory
The amount of context a model can accept within its supported input/output limits.
- Developer instructions Concept · Context & memory
Application-level instructions provided through a platform's developer role or equivalent mechanism.
- Episodic memory Concept · Context & memory
Records of previous events, attempts, observations, and outcomes.
- Few-shot prompting Pattern · Context & memory
Provide a small set of examples that demonstrate the desired behavior.
- In-context learning Concept · Context & memory
Adapt behavior from supplied context without updating model weights.
- Instruction hierarchy Concept · Context & memory
Rules that determine which instructions take precedence when messages conflict.
- Just-in-time context Concept · Context & memory
Load detailed information when needed rather than placing everything in the initial prompt.
- Long-term memory Concept · Context & memory
Information retained and retrieved across tasks or conversations.
- Memory consolidation Concept · Context & memory
Transform accumulated experiences into selected, reusable persistent information.
- Memory layers Concept · Context & memory
An informal architecture shorthand for separating memory by retention, scope, or function, such as working, episodic, semantic, and procedural memory.
- Memory scoping Concept · Context & memory
Partition memory by user, tenant, project, task, or permission boundary.
- Procedural memory Concept · Context & memory
Stored instructions describing how to perform tasks.
- Prompt chaining Pattern · Context & memory
Feed the output of one model step into a subsequent model step.
- Prompt engineering Concept · Context & memory
Design instructions, examples, and output requests to guide model behavior.
- Prompt optimization Pattern · Context & memory
Systematically improving prompts against a chosen objective using examples, evaluation, search, or programmatic optimization.
- Retrieval policy Concept · Context & memory
Rules for deciding what external information to fetch and what to include in context.
- Scratchpad Concept · Context & memory
Temporary working information used during a task; it may exist as explicit files or application state.
- Semantic memory Concept · Context & memory
Stored factual knowledge or relationships.
- System instructions Concept · Context & memory
High-priority instructions governing the assistant's behavior; exact role semantics depend on the platform.
- Working memory Concept · Context & memory
Task-local information currently available or selected for the ongoing interaction.
- Zero-shot prompting Pattern · Context & memory
Request a task without supplying task-specific demonstration examples.
Browse by topic
- Agents & architecture 23 — Who decides, what executes, and where control lives.
- Goals, specs & plans 23 — Describe the outcome before delegating the implementation.
- Loops, critics & adversaries 33 — Understand how work is challenged, repaired, and stopped.
- Multi-agent coordination 21 — Delegation, ownership, context boundaries, and aggregation.
- Context & memory 29 — What the model sees now, and what persists for later.
- Tools & output contracts 20 — Model proposals become validated, authorized operations.
- Protocols & interoperability 19 — Name the boundary: tools, agents, editors, or interfaces.
- Coding-agent internals 25 — Instructions, skills, hooks, tools, and durable artifacts.
- Retrieval & knowledge 31 — Find evidence, rank it, and preserve source boundaries.
- Evals & observability 29 — Measure outcomes and inspect the execution path.
- Inference & performance 28 — Latency, throughput, compute, and memory are different constraints.
- Models, reasoning & training 36 — Separate weight changes from context and inference-time work.
- Security & reliability 27 — Make privileges explicit and side effects recoverable.
- Generative UI & voice 18 — The interaction layer has its own contracts and timing.
- Generative & physical AI 8 — Broader model families beyond text-based assistants.
- Tools & ecosystem 44 — Recognize the role before choosing the dependency.