Context
Context is the difference between an AI that gives generic answers and one that knows your business. It's everything that enriches a raw input before it reaches the model — and it's where the most latent value hides.
What Context Covers
Context isn't one thing — it's a family of techniques that all serve the same goal: making generations relevant, grounded, and specific to your situation.
System Prompt
Developer-defined instructions that shape model behaviour, persona, and constraints. The invisible hand guiding every response.
Learn more →Prompt Schema
Structured prompt templates with variables, few-shot examples, and output format constraints. Turning ad-hoc prompting into repeatable engineering.
Learn more →RAG
Retrieval-Augmented Generation — grounding AI in your documents, knowledge bases, and internal data so it answers from facts, not guesses.
Learn more →Memory
Persistent context across conversations — user preferences, learned facts, conversation history. Agents that remember and improve over time.
Learn more →External Context
Search grounding, real-time data feeds, news APIs, and web search. When the answer isn't in your data, it's on the internet.
Learn more →What's the Difference?
These context types overlap but serve different purposes. Understanding the distinction helps you invest in the right layer.
RAG vs Memory
RAG retrieves from a static (but updateable) knowledge base — documents, wikis, internal data. Memory persists across conversations — what the agent learned from interacting with you. RAG is institutional knowledge. Memory is personal knowledge.
System Prompt vs Prompt Schema
A system prompt is a single set of instructions baked into every interaction. A prompt schema is a template with variables — it generates different prompts for different situations. System prompts are static. Schemas are dynamic.
Internal vs External Context
RAG and memory draw from your own data. External context pulls from the open web — search results, news feeds, public APIs. One grounds the agent in what you know. The other grounds it in what's happening now.
Context vs Inputs
Inputs are what the user (or program) sends. Context is what gets injected around those inputs to make them useful. The user types a question. Context adds the relevant documents, history, and instructions that make the answer good.
The Feedback Loop
Context is where the flywheel spins. The more your system generates, the richer the context becomes for the next generation.
Outputs become context
Every report, analysis, and structured output your agents create can be fed back into the context store — building institutional knowledge that compounds over time.
Conversations become memory
Past interactions teach agents about user preferences, domain terminology, and recurring patterns. Context-mining turns stored conversations into persistent knowledge.
Pairs With
Context enriches raw inputs. A bare question becomes an informed query when wrapped with RAG results, memory, and system instructions.
Storage feeds context. Stored outputs, conversations, and prompt libraries get mined back into the context store — the feedback loop that makes AI systems smarter over time.
Agents draw from context to make informed decisions. Without context, agents are generic. With it, they're domain experts.
Want to unlock the context layer?
I help build the context infrastructure that makes AI systems genuinely useful — RAG pipelines, memory systems, and the feedback loops that make them compound.