Mastering LLM Context Windows: Strategies for Long-Context Applications

The Context Window Challenge

Context windows have grown dramatically—from 4K tokens to 128K, 200K, even 1M+ tokens. But bigger isn't always better. Longer contexts mean higher costs, increased latency, and often degraded performance on information buried in the middle.

The real challenge isn't having enough context—it's using context effectively. This post covers battle-tested strategies for managing context in production applications.

Understanding Context Window Behavior

The "Lost in the Middle" Problem

Research shows LLMs struggle with information in the middle of long contexts. Performance is highest for information at the beginning and end, lowest in the middle.

Implications:

• Don't assume the model "sees" everything equally
• Position critical information strategically
• Consider multiple passes for comprehensive processing

Token Economics

Long contexts add up fast. A chatbot processing 50K tokens per conversation at 1000 conversations/day costs $125/day with GPT-4o vs.$7.50/day with GPT-4o-mini.

Latency Considerations

Latency scales with context length:

• Time to first token increases
• Total generation time increases
• Memory usage increases

For real-time applications, shorter effective context often beats longer theoretical context.

Context Compression Techniques

Context compression is the art of fitting more information into fewer tokens. Done well, it reduces costs without losing quality. Done poorly, it strips out the very information the model needs to answer correctly.

The compression-quality tradeoff: Every compression technique loses something. Summarization loses details. Selection loses context. The key is understanding what your task needs—if exact quotes matter, heavy summarization will hurt quality. If general understanding suffices, aggressive compression works fine.

When to compress vs. when to paginate: Compression assumes all information contributes to the answer. But for tasks like "find the date mentioned in this contract," most of the document is irrelevant. Paginated retrieval (find relevant chunks, ignore the rest) often outperforms compression (summarize everything) for specific lookups.

Extractive Compression

Select the most relevant portions of source material. This is "compression by omission"—keeping the best parts, discarding the rest:

TF-IDF/BM25 selection: Score sentences by relevance to query, include top-k.

Embedding-based selection: Embed query and candidate chunks, select by cosine similarity.

LLM-based selection: Ask model to identify relevant sections before detailed processing.

def compress_with_relevance(documents, query, max_tokens):
    # Embed query
    query_embedding = embed(query)

    # Score each chunk
    scored_chunks = []
    for doc in documents:
        for chunk in doc.chunks:
            score = cosine_similarity(query_embedding, chunk.embedding)
            scored_chunks.append((chunk, score))

    # Select top chunks within budget
    scored_chunks.sort(key=lambda x: x[1], reverse=True)
    selected = []
    total_tokens = 0
    for chunk, score in scored_chunks:
        if total_tokens + chunk.tokens <= max_tokens:
            selected.append(chunk)
            total_tokens += chunk.tokens

    return selected

Abstractive Compression

Summarize content to reduce tokens while preserving information:

Single-pass summarization:

Summarize this document in 500 words, preserving key facts and figures.

Query-focused summarization:

Summarize this document focusing on information relevant to: {query}

Hierarchical summarization: For very long documents, summarize sections, then summarize summaries.

Hybrid Compression

Combine extractive and abstractive:

1. Extract most relevant sections
2. Summarize less relevant but potentially useful sections
3. Combine extracts + summaries in context

Context Window Architectures

Different architectures handle long content in fundamentally different ways. Choosing the right architecture depends on your task: Does the answer require synthesizing information from across the document? Or is it localized to a specific section?

The synthesis problem: Some questions require connecting dots across a long document—"How did the company's strategy evolve over the five years covered in this report?" No single chunk contains the answer. You need to either fit everything in context (expensive) or use an architecture that synthesizes across chunks (complex).

The needle-in-haystack problem: Other questions have localized answers—"What was the Q3 revenue?" The answer exists in one place. Loading the entire document wastes tokens. Retrieval-based approaches excel here.

Sliding Window

Process content in overlapping windows. This is the simplest approach for handling documents longer than your context window:

[Document: 100K tokens]

Window 1: tokens 0-32K
Window 2: tokens 24K-56K (8K overlap)
Window 3: tokens 48K-80K
Window 4: tokens 72K-100K

Aggregate results from all windows

Use cases:

• Searching for specific information
• Extracting all instances of something
• Parallel processing for speed

Limitations:

• May miss cross-window patterns
• Aggregation complexity
• Overlap adds redundancy

Hierarchical Processing

Process at multiple levels of abstraction:

Level 3: Document summary (500 tokens)
Level 2: Section summaries (200 tokens each)
Level 1: Paragraph chunks (500 tokens each)
Level 0: Raw text

Query routing:
- Simple questions → Level 3
- Section-specific → Level 2
- Detail needed → Level 1 retrieval
- Exact quotes → Level 0 retrieval

Map-Reduce Processing

For questions requiring synthesis across large documents:

Map phase: Process each chunk independently with the same query.

Reduce phase: Combine chunk-level results into final answer.

def map_reduce_qa(document, question):
    chunks = split_document(document, chunk_size=8000)

    # Map: Process each chunk
    chunk_answers = []
    for chunk in chunks:
        answer = llm.query(f"""
            Based on this text, answer the question if relevant information is present.
            If no relevant information, respond "No relevant information."

            Text: {chunk}
            Question: {question}
        """)
        if answer != "No relevant information.":
            chunk_answers.append(answer)

    # Reduce: Synthesize answers
    if not chunk_answers:
        return "No relevant information found."

    final_answer = llm.query(f"""
        Synthesize these partial answers into a comprehensive response:

        Partial answers:
        {chr(10).join(chunk_answers)}

        Question: {question}
    """)
    return final_answer

Recursive Processing

For deep analysis of long content:

def recursive_analyze(text, depth=0, max_depth=3):
    if len(text) <= MAX_CONTEXT:
        return llm.analyze(text)

    # Split into manageable chunks
    chunks = split_text(text)

    # Analyze each chunk
    chunk_analyses = [recursive_analyze(c, depth+1, max_depth) for c in chunks]

    # Synthesize if not at max depth
    if depth < max_depth:
        return llm.synthesize(chunk_analyses)
    else:
        return chunk_analyses

Memory Systems for Infinite Context

Conversation Memory

For long conversations that exceed context limits:

Rolling window: Keep last N turns, summarize older turns.

Summary + recent:

[Conversation summary: 500 tokens]
[Last 5 turns: 2000 tokens]
[Current turn: 500 tokens]

Episodic memory: Store key moments/decisions, retrieve when relevant.

Long-Term Memory

Persist information across sessions:

Vector memory: Store embeddings of important information, retrieve by similarity.

Structured memory:

{
  "user_preferences": {"tone": "casual", "detail_level": "high"},
  "established_facts": ["user is in EST timezone", "prefers email"],
  "ongoing_tasks": [{"task": "quarterly report", "status": "50%"}],
  "key_decisions": [{"date": "2024-11", "decision": "chose Plan B"}]
}

Hybrid memory: Structured for known schemas, vector for unstructured recall.

Memory Management

Memory grows unboundedly without management:

Importance scoring: Not all memories are equal. Score by recency, frequency of access, explicit importance markers.

Consolidation: Periodically merge similar memories, summarize details, archive rarely-accessed content.

Forgetting: Delete low-importance memories that haven't been accessed. Implement graceful degradation.

Practical Strategies

Context Budget Allocation

For a 32K token budget:

System prompt:        2K tokens (6%)
Retrieved context:   20K tokens (63%)
Conversation history: 6K tokens (19%)
User query + buffer:  4K tokens (12%)

Adjust based on application needs. RAG-heavy apps need more retrieval budget. Conversational apps need more history.

Dynamic Context Selection

Not all queries need full context:

def select_context(query, available_context, budget):
    # Classify query complexity
    complexity = classify_complexity(query)

    if complexity == "simple":
        # Light context, fast response
        return select_top_k(available_context, k=3, budget=budget//4)
    elif complexity == "moderate":
        # Balanced context
        return select_top_k(available_context, k=10, budget=budget//2)
    else:
        # Complex query, full context
        return select_top_k(available_context, k=30, budget=budget)

Context Caching

Avoid reprocessing the same context:

Prompt caching (provider-level): OpenAI and Anthropic offer prompt caching—identical prefixes are cached and charged at reduced rates.

Application-level caching:

class ContextCache:
    def __init__(self):
        self.cache = {}  # hash -> processed context

    def get_or_process(self, raw_context, processor):
        cache_key = hash(raw_context)
        if cache_key in self.cache:
            return self.cache[cache_key]

        processed = processor(raw_context)
        self.cache[cache_key] = processed
        return processed

Streaming with Context

For long-context processing, stream results:

async def stream_long_document_analysis(document):
    chunks = split_document(document)

    for i, chunk in enumerate(chunks):
        yield f"Processing section {i+1}/{len(chunks)}...\n"

        analysis = await analyze_chunk(chunk)
        yield f"Section {i+1} findings:\n{analysis}\n\n"

    yield "Synthesizing final analysis...\n"
    final = await synthesize(all_analyses)
    yield f"Final analysis:\n{final}"

Evaluation and Optimization

Measuring Context Effectiveness

Track metrics:

• Recall: Does the context contain the needed information?
• Precision: What fraction of context is actually used?
• Utilization: How much of provided context influences the response?

A/B Testing Context Strategies

Compare:

• Different chunk sizes
• Different retrieval counts
• Different compression methods
• Different context ordering

Measure on answer quality, latency, and cost.

Monitoring Context Usage

class ContextMonitor:
    def log_request(self, request):
        self.log({
            "context_tokens": request.context_tokens,
            "response_tokens": request.response_tokens,
            "query_type": request.query_type,
            "retrieval_count": len(request.retrieved_docs),
            "latency_ms": request.latency,
            "quality_score": request.quality_score
        })

    def analyze(self):
        # Find optimal context size by query type
        # Identify over/under-contextualization
        # Track cost trends

Advanced Techniques

Attention Steering

Guide model attention to important context:

Structural markers:

[CRITICAL INFORMATION]
The deadline is December 15, 2024.
[END CRITICAL]

Explicit instructions:

Pay particular attention to the dates and deadlines mentioned in the context.

Multi-Document Reasoning

When context spans multiple documents:

Source attribution:

Document 1 (Q3 Report): Revenue grew 15%
Document 2 (Press Release): New product launched
Document 3 (Analyst Note): Market share increased

Synthesize while maintaining source clarity.

Conflict resolution: When documents disagree, explicitly handle:

Document 1 says X, but Document 2 says Y.
Based on [criteria], Document 1 is more reliable because...

Progressive Disclosure

For complex queries, build understanding progressively:

Pass 1: High-level overview from summaries
Pass 2: Deep dive into relevant sections
Pass 3: Extract specific details as needed
Pass 4: Synthesize final response

Each pass uses targeted context, not full dump.

Production Patterns

Graceful Degradation

When context exceeds limits:

def process_with_degradation(context, query, budget):
    if token_count(context) <= budget:
        return full_process(context, query)

    # Try compression
    compressed = compress(context, target=budget)
    if token_count(compressed) <= budget:
        return process_with_note(compressed, query, "Context was compressed")

    # Fall back to retrieval-only
    retrieved = retrieve_top_k(context, query, k=5)
    return process_with_note(retrieved, query, "Using most relevant excerpts only")

Context Versioning

For reproducibility:

context_version = {
    "raw_sources": ["doc1_v2", "doc2_v1"],
    "processing": "compression_v3",
    "selection": "top_10_by_similarity",
    "timestamp": "2024-12-05T10:30:00Z",
    "token_count": 15420
}

Error Handling

Context-related failures:

try:
    response = llm.complete(prompt, context)
except ContextTooLongError:
    # Compress and retry
    compressed_context = compress(context, target=0.5)
    response = llm.complete(prompt, compressed_context)
except ContextProcessingError:
    # Fall back to no context
    response = llm.complete(prompt, context=None)
    response.note = "Generated without full context due to processing error"

Conclusion

Effective context management is essential for production LLM applications. The key insights:

1. More context ≠ better results. Carefully curate what goes into context.
2. Position matters. Put critical information at the start and end.
3. Compression is your friend. Summarize aggressively, retrieve precisely.
4. Build for the common case. Most queries don't need maximum context.
5. Monitor and optimize. Track context utilization and cost continuously.

The best context management is invisible—users get accurate, fast responses without knowing the engineering that made it possible.