How Context Engineering Solves the AI Knowledge Problem

Large Language Models are brilliant — until they're not. One moment your AI assistant writes flawless code, the next it hallucinates an API that doesn't exist. The difference isn't the model. It's the context.

We've all been there: you paste a question into ChatGPT, get a confident response, and then spend an hour debugging because the model had no idea about your project's architecture, your company's conventions, or the specific version of the framework you're using. This is the context problem, and it's the single biggest bottleneck in AI-assisted development today.

Context Engineering is the discipline of designing systems that deliver the right information to AI models at exactly the right time. It's not prompt engineering (though prompts are part of it). It's the entire pipeline — from how documentation is collected, processed, and indexed, to how it's retrieved and formatted when an AI needs it.

The Problem: AI Without Context is Just Autocomplete

Consider this scenario: you're building a service that integrates with AWS Lambda. You ask your AI coding assistant to help you write a deployment function. Without context, it might:

• Use a deprecated SDK version (aws-sdk v2 instead of @aws-sdk/client-lambda v3)
• Suggest API parameters that were removed two versions ago
• Hallucinate function signatures that look plausible but don't exist
• Miss your team's established patterns for error handling and logging

The model isn't stupid. It was trained on millions of code repositories, blog posts, and docs. But training data is frozen in time, and your project is alive. The gap between what the model knows and what it needs to know is the context gap.

What is Context Engineering?

Context Engineering is the practice of building systems that curate, transform, and deliver knowledge to AI models. It sits at the intersection of information retrieval, knowledge management, and AI infrastructure.

A good context engineering system does three things:

CoreDoc: Context Engineering in Practice

This is exactly what we built with CoreDoc — a context engineering platform that turns scattered documentation into a structured, searchable knowledge base that AI tools can query programmatically.

CoreDoc isn't a chatbot. It's not a RAG wrapper around a vector database. It's a full-stack documentation intelligence system with four complementary search interfaces, each optimized for a different retrieval pattern:

GET /api/v1/coredoc/read?file_path=lambda/latest/api/API_CreateFunction.html

Response:
{
  "title": "CreateFunction",
  "lines": [
    "  1 | # CreateFunction",
    "  2 | ",
    "  3 | Creates a new Lambda function..."
  ]
}

GET /api/v1/coredoc/glob?pattern=**/lambda/**/*.html

→ Discovers all Lambda-related HTML docs across all libraries

GET /api/v1/coredoc/grep?pattern=CreateFunction.*timeout&context=3

→ Finds exact regex matches with surrounding context lines

GET /api/v1/coredoc/semantic?query=how to configure lambda timeout

→ Returns ranked results by relevance with ts_rank scoring

Why Four Search Modes? Because AI Thinks Differently Than You

Most documentation tools are built for humans browsing a website. But when an AI coding assistant needs context, it doesn't browse — it queries. And different situations demand different query strategies:

This is the Discover → Search → Read workflow. An AI agent first discovers what's available (Glob), then searches for relevant content (Grep or Semantic), then reads the full document (Read). Each step narrows the context window to exactly what's needed.

The Context Pipeline: From Web to Knowledge Base

CoreDoc doesn't just index static files. It has a full ingestion pipeline that crawls, processes, and structures documentation from multiple source types:

Real Impact: What Changes When AI Has Good Context

When you connect an AI coding assistant to CoreDoc, the difference is immediate and dramatic:

Knowledge Libraries: Organized by Domain

CoreDoc organizes documentation into Libraries — logical collections like aws/docs, react/docs, or your-company/internal. Within each library, Document Groups track where content came from:

• Sitemap groups — crawled from XML sitemaps (e.g., AWS documentation)
• Channel groups — ingested from content channels (YouTube, RSS)
• Repo groups — synced from GitHub repositories

This provenance tracking means you always know where your context came from, when it was last updated, and how many documents exist in each group. No more mystery knowledge bases where you can't trace the source.

The Philosophy: Familiar Tools, Superhuman Scale

You'll notice that CoreDoc's search tools mirror Unix utilities: read, glob, grep. This is intentional. Developers already understand these mental models. An AI coding assistant that knows how to grep a codebase can immediately grep a documentation base with the same interface.

The difference is scale. You can't grep the entire AWS documentation locally. But CoreDoc can, in milliseconds, thanks to PostgreSQL trigram indexes. You can't glob across 10,000 documentation pages on disk. But CoreDoc can, using indexed path-based queries.

Looking Forward: The Context-First Future

We're at an inflection point in AI-assisted development. The models will keep getting better, context windows will keep growing, and agents will become more capable. But none of that matters if the context pipeline is broken.

The teams that invest in context engineering today — building robust knowledge bases, creating retrieval APIs, organizing their documentation — will be the ones that get 10x more value from every AI advancement tomorrow.

CoreDoc is our contribution to this future. It's open, it's API-first, and it treats documentation as critical infrastructure rather than an afterthought.

Because the best AI isn't the one with the most parameters. It's the one with the best context.