Context Engineering: The New Frontier for AI Teams in 2025

Juan Luis Ramirez8 min read
Context EngineeringAIData ScienceLLMRAG

If you've been building with LLMs over the past year, you've probably noticed something interesting: the best prompt in the world won't save you if you're feeding garbage context to your model. Welcome to 2025, where context engineering is quickly becoming the make-or-break skill for AI teams.

Let me explain why this shift is happening and, more importantly, how to get good at it.

The Prompt Engineering Plateau

Remember when everyone was obsessed with finding the perfect prompt? "Act as an expert..." or "Think step by step..." seemed like magical incantations that could unlock better model performance. And to be fair, they worked - for a while.

But here's what we learned: once you've got a decent prompt template, you hit a ceiling pretty quickly. The real bottleneck isn't how you ask the question. It's what information the model has access to when it answers.

Think about it this way: if I asked you to explain quantum computing but only gave you access to a cookbook, no amount of creative questioning would help. You need the right context.

What Exactly Is Context Engineering?

Context engineering is the practice of systematically designing, curating, and optimizing the information you provide to LLMs before they generate responses. It's about being intentional with every token that goes into your context window.

This includes:

  • Selecting which documents to retrieve from your knowledge base
  • Determining how to chunk and structure that information
  • Deciding what metadata to include
  • Crafting the system message and conversation history
  • Managing the token budget across all these components

Unlike prompt engineering, which is mostly about phrasing and structure, context engineering is deeply technical. It requires understanding data pipelines, search algorithms, and how models actually process information.

Why Context Engineering Matters Now

Three trends converged to make context engineering critical:

1. Longer context windows: Models went from 4K to 128K+ tokens almost overnight. Suddenly we're not fighting for space - we're fighting for relevance in a sea of information. GPT-4 Turbo, Claude 2.1, and Gemini 1.5 all support massive contexts, but that doesn't mean you should fill them randomly.

2. RAG became mainstream: Retrieval-Augmented Generation is now the standard architecture for production LLM apps. But RAG is only as good as what you retrieve and how you present it. Bad retrieval means bad context, which means bad outputs - regardless of your model choice.

3. Cost and latency reality: Every token you send costs money and adds latency. A poorly engineered context might use 50K tokens when 5K would work better. That's a 10x difference in both speed and cost.

The Core Components of Context Engineering

Let's break down what you actually need to optimize:

1. Data Curation and Preparation

Your context is only as good as your source data. This means:

# Bad: Throwing everything at the model
context = "\n".join([doc.text for doc in all_documents])
 
# Good: Curated, structured, and relevant
context = create_optimized_context(
    query=user_query,
    documents=filtered_docs,
    metadata=["source", "timestamp", "category"],
    max_tokens=4000
)

Key practices:

  • Clean your data before indexing (remove boilerplate, deduplicate, fix encoding)
  • Structure information hierarchically (summaries before details)
  • Include relevant metadata (dates, sources, confidence scores)
  • Remove or minimize irrelevant information

2. Intelligent Retrieval Strategies

Not all retrieval is created equal. Here's what modern context engineering looks like:

from langchain.retrievers import EnsembleRetriever
from langchain.retrievers import BM25Retriever, VectorStoreRetriever
 
# Hybrid search: semantic + keyword
vector_retriever = vectorstore.as_retriever(search_kwargs={"k": 10})
bm25_retriever = BM25Retriever.from_documents(documents)
 
ensemble_retriever = EnsembleRetriever(
    retrievers=[bm25_retriever, vector_retriever],
    weights=[0.3, 0.7]  # Tune based on your use case
)

Advanced teams are also implementing:

  • Multi-stage retrieval: Cast a wide net, then rerank with a cross-encoder
  • Query expansion: Generate multiple variations of the user's question
  • Contextual compression: Summarize retrieved chunks that are relevant but verbose
  • Citation tracking: Keep metadata to cite sources in responses

3. Context Window Management

With 128K token windows, you might think you can just dump everything in. Don't. Here's why:

def optimize_context_budget(
    query: str,
    retrieved_docs: list,
    max_tokens: int = 8000  # Leave room for the response
) -> str:
    """
    Intelligent context allocation based on relevance
    """
    # Reserve tokens for different components
    system_prompt_tokens = 200
    query_tokens = count_tokens(query)
    response_budget = 2000
 
    available_for_docs = max_tokens - system_prompt_tokens - query_tokens - response_budget
 
    # Allocate based on relevance scores
    context_parts = []
    token_count = 0
 
    for doc in sorted(retrieved_docs, key=lambda x: x.score, reverse=True):
        doc_tokens = count_tokens(doc.text)
        if token_count + doc_tokens <= available_for_docs:
            context_parts.append(doc.text)
            token_count += doc_tokens
        else:
            # Try compressed version
            compressed = summarize_document(doc.text)
            if token_count + count_tokens(compressed) <= available_for_docs:
                context_parts.append(compressed)
                token_count += count_tokens(compressed)
 
    return "\n\n---\n\n".join(context_parts)

Research shows models pay more attention to information at the beginning and end of the context (the "lost in the middle" problem). Put your most important context there.

4. Context Quality Over Quantity

This is counterintuitive, but smaller, more relevant context often outperforms larger, kitchen-sink approaches:

# Quality metrics to track
context_quality = {
    "relevance_score": calculate_similarity(query, context),
    "diversity": measure_topic_coverage(context),
    "redundancy": detect_duplicate_information(context),
    "coherence": check_logical_flow(context),
    "token_efficiency": relevant_info_per_token(context)
}

Measuring Context Engineering Success

You can't improve what you don't measure. Here are the key metrics:

Retrieval metrics:

  • Precision@K (how many retrieved docs are relevant)
  • Recall@K (what percentage of relevant docs you retrieved)
  • MRR (Mean Reciprocal Rank - where the first relevant doc appears)

Context quality metrics:

  • Answer accuracy (does the model get it right?)
  • Hallucination rate (does it make stuff up?)
  • Citation accuracy (does it cite the right sources?)
  • Latency and cost per query

Real-world example:

from ragas import evaluate
from ragas.metrics import faithfulness, answer_relevancy, context_precision
 
# Evaluate your RAG pipeline
results = evaluate(
    dataset=test_questions,
    metrics=[faithfulness, answer_relevancy, context_precision]
)
 
print(f"Faithfulness: {results['faithfulness']}")  # Target: > 0.8
print(f"Context Precision: {results['context_precision']}")  # Target: > 0.7

The Context Engineering Toolkit

Here are the tools and frameworks that make context engineering practical:

For retrieval and indexing:

  • LlamaIndex (best for complex, multi-document retrieval)
  • LangChain (great ecosystem, lots of integrations)
  • Weaviate, Pinecone, Qdrant (vector databases)
  • Elasticsearch, Typesense (hybrid search)

For evaluation:

  • RAGAS (RAG assessment framework)
  • Phoenix Arize (observability for embeddings)
  • LangSmith (LangChain's tracing platform)
  • Custom evaluation datasets (build your own ground truth)

For optimization:

  • LongLLMLingua (intelligent context compression)
  • Cohere Rerank (better document ordering)
  • HyDE (Hypothetical Document Embeddings for better retrieval)

Practical Best Practices

After building production RAG systems, here's what actually works:

1. Start with small, high-quality context Get 100 tokens of perfect information before experimenting with 10K tokens of noise.

2. Test retrieval independently Before you involve the LLM, make sure your retrieval is actually returning relevant documents. Use human evaluation for the first 50-100 queries.

3. Version your embeddings and chunks When you change how you chunk or embed documents, you need to re-index everything. Treat this like a database migration.

4. Build feedback loops

# Track what actually gets used
def log_context_usage(query, context, response):
    # Which parts of context did the model cite?
    # Was the answer good?
    # Would different context have helped?
    analytics.track({
        "query": query,
        "context_chunks": len(context),
        "cited_chunks": extract_citations(response),
        "user_rating": get_user_feedback()
    })

5. Don't ignore the system prompt Your system message is part of your context. Use it to set expectations about how to use the provided information:

You are a helpful assistant. You will be provided with context from our documentation.
Always cite your sources using [Source: X] format.
If the context doesn't contain the answer, say so clearly.
Prioritize recent information over outdated content.

The Future of Context Engineering

Where is this headed? A few predictions:

Automated context optimization: AI systems that learn which context configurations work best for different query types. We're already seeing this with techniques like DSPy that programmatically optimize prompts and context.

Multi-modal context: Combining text, images, tables, and code into coherent context packages. Models like GPT-4V and Gemini Ultra are making this practical.

Real-time context adaptation: Systems that adjust context strategy based on query complexity, user intent, and available resources.

Context as infrastructure: Just like we have databases and APIs, we'll have specialized "context layers" that different applications can plug into.

Getting Started

If you're building with LLMs today, here's your action plan:

  1. Audit your current context: What are you actually sending to your model? Is it necessary? Is it relevant?

  2. Measure before optimizing: Set up basic metrics for retrieval quality and answer accuracy

  3. Start simple: Hybrid search + reranking gets you 80% of the way there

  4. Iterate with data: Use real user queries to improve your retrieval and ranking

  5. Think systematically: Context engineering isn't a one-time optimization - it's an ongoing discipline

The teams that master context engineering will build faster, cheaper, and more reliable AI products. The ones that don't will keep throwing money at bigger models and longer context windows, wondering why their accuracy isn't improving.

Start small, measure everything, and remember: the best context is the one that gives your model exactly what it needs - nothing more, nothing less.

Want to dive deeper into RAG systems? Check out my practical guide to getting started with RAG, where we build a complete retrieval pipeline from scratch.

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