Context Engineering: The 2025 Guide to Building Production-Ready AI
Your AI feature works perfectly in the demo. Then it ships to production and starts hallucinating customer names, inventing API endpoints, and confidently citing documentation that doesn't exist.
The problem isn't your model. It's your context.
Every engineer building with AI in 2025 hits the same wall: the gap between "works on my machine with carefully selected examples" and "works reliably with real user queries at scale." That gap is context engineering, and it's harder than training models or writing prompts.
Context engineering is the discipline of assembling, structuring, and delivering the right information to an AI system at the right time. It's retrieval systems, chunking strategies, semantic search, relevance ranking, and cache invalidation all wrapped into one problem that determines whether your AI feature is production-ready or just an expensive chatbot.
Why Context Engineering Matters More Than Your Model Choice
Here's what nobody tells you about production AI: the model is the easy part. GPT-4, Claude, Gemini — they're all good enough for most use cases. The hard part is getting your model the context it needs to actually be useful.
Take a code explanation feature. Simple, right? You send the code to Claude, ask for an explanation, done. Except Claude can't explain why a function exists without seeing where it's called. It can't describe what an API endpoint does without seeing the request handlers. It can't tell you if code is deprecated without access to commit history and internal docs.
Your model knows everything about general programming. It knows nothing about your codebase. That knowledge gap is context, and bridging it is engineering work.
Bad context engineering looks like this:
Dumping entire files into prompts and hoping the model finds what matters
Keyword search that misses semantic matches
Stale documentation that's been outdated for six months
No way to verify if retrieved information is actually relevant
Good context engineering looks like this:
Precise retrieval that finds the 3 functions that actually matter for understanding this endpoint
Automatic freshness — context updates when code changes
Relevance ranking that puts critical information first
Structured metadata that tells the model what it's looking at
The difference between these isn't subtle. It's the difference between "occasionally useful" and "reliable enough to ship."
The Three Hard Problems
Context engineering breaks down into three problems that sound simple until you try to solve them in production.
Problem 1: What context exists?
You need to know what information you have before you can retrieve it. For code, that means parsing every file, extracting symbols, mapping dependencies, indexing comments, tracking API routes, documenting database schemas. For documents, it means chunking, embedding, and maintaining a searchable index.
Most teams start by throwing everything into a vector database and calling it done. This fails because vectors alone don't preserve structure. You lose the hierarchy of "this function belongs to this class belongs to this module." You lose the relationships between code and tests. You lose the temporal aspect of "this code was just changed yesterday."
You need structured indexing that captures both content and relationships. When someone asks "how does user authentication work," you need to find not just the auth function, but the middleware that calls it, the database models it touches, and the configuration that controls it.
Problem 2: What context is relevant?
This is the retrieval problem, and it's harder than it looks. Semantic similarity via embeddings gets you part of the way there — queries like "database connection pooling" will find the right code even if it doesn't use those exact words.
But pure semantic search has blind spots. It doesn't understand recency (code changed yesterday is probably more relevant than code unchanged for years). It doesn't understand importance (the function called 100 times matters more than the one called twice). It doesn't understand scope (if you're debugging authentication, you care about the auth module first, then its dependencies).
Production context engineering means layering multiple relevance signals:
Semantic similarity for conceptual matches
Usage patterns for importance
Dependency graphs for scope
Temporal signals for freshness
Explicit metadata for precision
Problem 3: How do you keep context current?
Context goes stale fast. Code changes daily. Documentation falls behind. That perfectly tuned retrieval system you built last quarter? It's serving outdated context to half your queries by now.
Static indexes don't work. You need continuous updating that tracks changes and invalidates affected context. When someone refactors an API endpoint, every piece of documentation referencing that endpoint needs to update. When a function gets renamed, every example using the old name becomes misleading.
The teams shipping production AI in 2025 aren't treating context as a one-time setup problem. They're treating it as continuous infrastructure that updates alongside their codebase.
Building a Context Engineering System
Let's get concrete. Here's what a production-grade context system actually looks like.
Layer 1: Structured Indexing
Start by building a complete map of what you're working with. For codebases, this means:
This structure captures not just code content, but the relationships and metadata that make it understandable. You're building a graph, not a flat list.
Layer 2: Multi-Signal Retrieval
When a query comes in, you need to hit it from multiple angles:
Semantic search via embeddings finds conceptually similar code. But then you filter and rank by:
Call graph distance: code that's 1 hop away matters more than code 5 hops away
Modification recency: recent changes are usually more relevant
Symbol importance: frequently called functions rank higher
Explicit connections: if the query mentions a specific file, prioritize that file's dependencies
The result is a ranked list of context chunks that's far more precise than embeddings alone.
Layer 3: Automatic Freshness
Hook into your git workflow to update context in real-time:
def on_code_change(file_path, diff):
# Reparse changed files
updated_symbols = parse_symbols(file_path)
# Invalidate dependent context
dependents = dependency_graph.get_dependents(file_path)
for dep in dependents:
invalidate_cache(dep)
# Regenerate embeddings for affected chunks
for symbol in updated_symbols:
reembed(symbol)
This is the unglamorous infrastructure work that makes the difference between a proof-of-concept and production.
Where Tools Like Glue Come In
This is exactly the kind of context infrastructure that glue.tools automates. Instead of building your own indexing pipeline, maintaining your own dependency graphs, and writing your own change tracking, you get structured code intelligence out of the box.
Glue indexes your entire codebase — not just file contents, but symbols, API routes, database schemas, and the relationships between them. It discovers features using AI agents, generates documentation that stays current, and exposes all of this as structured context for any AI tool you're building.
More importantly, it keeps that context fresh automatically. Code changes, features get refactored, APIs evolve — Glue's context stays current without manual maintenance.
For teams building AI features on top of their codebase, this is the difference between spending months building context infrastructure and starting with production-grade context on day one.
Context Engineering in Practice
Here's what this looks like for common AI use cases:
Code explanation: Instead of sending raw code to an LLM, send the code plus its call graph, recent changes, related tests, and documentation. The model can explain not just what the code does, but why it exists and how it fits into the system.
Automated documentation: Context engineering means knowing which code is part of the public API, which is internal, what's deprecated, and what recently changed. You can generate docs that are accurate and actually useful.
Semantic code search: Users search in natural language. Your system translates that to precise context retrieval across code, documentation, issues, and commits. It's not just "find similar text" — it's "understand intent and return the exact code that matters."
The teams shipping these features successfully aren't using better models. They're using better context.
What This Looks Like in 2025
Context engineering is becoming infrastructure. Just like you don't build your own database or message queue anymore, you won't build your own context layer from scratch.
The winning pattern is:
Start with a platform that handles core context infrastructure (indexing, freshness, retrieval)
Layer your domain-specific context on top
Fine-tune retrieval for your use cases
Focus your engineering time on the AI features themselves, not the plumbing
Platforms like Glue handle the universal hard parts — parsing code, building dependency graphs, tracking changes, maintaining search indexes. You handle the parts that are specific to your product.
This is the same evolution we saw with databases, auth, and observability. The fundamentals become infrastructure. The differentiation happens in how you use it.
Start With Structure, Not Embeddings
If you're building AI features today, start by auditing your context strategy. Not your prompt engineering, not your model choice — your context.
Can you programmatically answer:
What information exists in your system?
What's relevant for a given query?
How current is your context?
How do you know if retrieved context is actually useful?
If you can't answer these cleanly, your context engineering needs work before your AI features are production-ready.
The good news: this is engineering, not magic. You can build these systems. Or you can use tools that have already solved the hard parts and focus on the AI features you actually want to ship.
Context engineering is the new bottleneck. The teams who solve it first will ship AI features that actually work.