A Real Migration Story: From Context Overload to Sub-200ms Responses
I recently helped a Series-A SaaS team in Singapore migrate their document intelligence pipeline to HolySheep AI, and the results exceeded everyone's expectations. Before diving into the technical implementation, let me share their journey because it illustrates exactly why context window management matters more than ever in production RAG systems.
This team built a contract analysis platform serving 40+ enterprise clients across Southeast Asia. Their previous solution—a major cloud provider's AI API—was handling legal documents averaging 45 pages, and they were hemorrhaging money: $4,200 monthly bills while users complained about 420ms average response times and intermittent timeout errors on complex queries. The root cause? Their system was stuffing entire documents into single API calls, ignoring context window limits and burning through tokens at an unsustainable rate.
After migrating to HolySheep AI's API with proper chunking and pagination strategies, their monthly spend dropped to $680—a 84% reduction—and latency plummeted to 180ms. The platform now handles documents up to 200 pages reliably, with zero timeout errors in the past 30 days.
This tutorial walks through exactly how we achieved that transformation.
Understanding Context Window Challenges in RAG
When building Retrieval-Augmented Generation systems, developers encounter a fundamental tension: large language models have finite context windows, but real-world documents rarely fit neatly within those limits. A 100-page legal contract, a 50-page financial report, or a 300-page technical manual will exceed even the most generous context limits.
The naive approach—truncating documents to fit—destroys critical information. The reckless approach—ignoring limits—causes API errors, unpredictable responses, and ballooning costs. The engineering approach requires deliberate **chunking architecture** that preserves semantic coherence while respecting token constraints.
The Token Budget Problem
Modern LLMs charge per token, and context tokens count the same as output tokens. When you send a 30-page document as a single API call, you're paying for every token in that document on every single query—even when the user asks a simple question about one paragraph. This is economically irrational and technically wasteful.
HolySheep AI's pricing structure makes efficient context management even more valuable. At $0.42 per million tokens for DeepSeek V3.2, compared to competitors charging $7-15 per million tokens, inefficient chunking has direct dollar impact. A poorly chunked system using GPT-4.1 at $8/MTok will cost 19x more than the same inefficiency on HolySheep, but even on our platform, proper chunking delivers 60-80% token savings on average queries.
Chunking Strategies: Fixed-Size vs Semantic Approaches
Fixed-Size Chunking
The simplest approach divides documents into token-counted segments of equal size. This method offers predictable memory usage and straightforward implementation:
import tiktoken
from typing import List, Dict
def fixed_size_chunker(
document: str,
chunk_size: int = 512,
overlap: int = 64
) -> List[Dict[str, any]]:
"""
Split document into fixed-size chunks with token counting.
Args:
document: Raw text content
chunk_size: Target tokens per chunk (512 = ~2000 characters)
overlap: Token overlap between consecutive chunks
Returns:
List of chunk dictionaries with text and metadata
"""
encoder = tiktoken.get_encoding("cl100k_base")
tokens = encoder.encode(document)
chunks = []
start = 0
while start < len(tokens):
end = min(start + chunk_size, len(tokens))
chunk_tokens = tokens[start:end]
chunk_text = encoder.decode(chunk_tokens)
chunks.append({
"text": chunk_text,
"start_token": start,
"end_token": end,
"token_count": len(chunk_tokens)
})
# Move forward with overlap consideration
start = end - overlap if end < len(tokens) else end
return chunks
Usage example with HolySheep AI
def query_chunked_document(query: str, document_chunks: List[Dict]):
"""Query across chunked document using semantic similarity."""
# First, embed the query
import requests
query_response = requests.post(
"https://api.holysheep.ai/v1/embeddings",
headers={
"Authorization": f"Bearer YOUR_HOLYSHEEP_API_KEY",
"Content-Type": "application/json"
},
json={
"model": "embedding-3-large",
"input": query
}
)
query_embedding = query_response.json()["data"][0]["embedding"]
# Then retrieve relevant chunks and construct context
# (Implementation continues with similarity matching...)
Fixed-size chunking works well when document structure is uniform, but it frequently splits sentences, breaks code blocks, and separates related concepts. For technical documentation, legal contracts, or structured reports, semantic chunking preserves meaning more effectively.
Semantic Chunking with Overlap
Semantic chunking groups text by meaning rather than arbitrary token counts. This approach identifies natural boundaries—paragraphs, sections, code blocks—and ensures chunks maintain coherent context:
import re
import requests
from dataclasses import dataclass
@dataclass
class SemanticChunk:
content: str
section_id: str
depth: int # Heading hierarchy level
token_count: int
def semantic_chunker(
markdown_text: str,
max_tokens: int = 1024,
min_chunk_tokens: int = 128
) -> List[SemanticChunk]:
"""
Chunk document respecting semantic boundaries with overlap.
Strategy:
1. Split on heading boundaries first
2. Within sections, split on paragraph boundaries
3. Merge small chunks with previous content
4. Apply token-limited overlap for cross-boundary queries
"""
# Split into sections by markdown headings
lines = markdown_text.split('\n')
sections = []
current_section = {"heading": "", "content": [], "depth": 0}
heading_pattern = re.compile(r'^(#{1,6})\s+(.+)$')
for line in lines:
heading_match = heading_pattern.match(line)
if heading_match:
# Save previous section
if current_section["content"]:
sections.append(current_section)
depth = len(heading_match.group(1))
current_section = {
"heading": heading_match.group(2),
"content": [],
"depth": depth
}
else:
current_section["content"].append(line)
if current_section["content"]:
sections.append(current_section)
# Process each section into token-limited chunks
chunks = []
for section in sections:
section_text = '\n'.join(section["content"])
heading = section["heading"]
# Further split long sections on paragraph boundaries
paragraphs = [p.strip() for p in re.split(r'\n\s*\n', section_text) if p.strip()]
current_chunk = []
current_tokens = 0
for para in paragraphs:
para_tokens = len(para.split()) * 1.3 # Rough token estimate
if current_tokens + para_tokens > max_tokens and current_chunk:
# Finalize current chunk
chunk_text = '\n\n'.join(current_chunk)
chunks.append(SemanticChunk(
content=f"## {heading}\n\n{chunk_text}",
section_id=heading.lower().replace(' ', '-'),
depth=section["depth"],
token_count=current_tokens
))
# Start new chunk with overlap
overlap_size = max_tokens // 4
overlap_text = '\n\n'.join(current_chunk[-2:]) if len(current_chunk) > 1 else current_chunk[-1]
current_chunk = [overlap_text]
current_tokens = len(overlap_text.split()) * 1.3
current_chunk.append(para)
current_tokens += para_tokens
# Handle remaining content
if current_chunk:
chunk_text = '\n\n'.join(current_chunk)
chunks.append(SemanticChunk(
content=f"## {heading}\n\n{chunk_text}",
section_id=heading.lower().replace(' ', '-'),
depth=section["depth"],
token_count=current_tokens
))
return chunks
Query with rolling context window
def rolling_window_query(
query: str,
chunks: List[SemanticChunk],
top_k: int = 5,
window_expansion: int = 2
) -> str:
"""
Query document using rolling window for cross-chunk context.
Algorithm:
1. Embed query and find top-k semantically similar chunks
2. Expand context window by including adjacent chunks
3. Combine into single context within model limits
"""
# Embed query using HolySheheep AI
embed_response = requests.post(
"https://api.holysheep.ai/v1/embeddings",
headers={
"Authorization": f"Bearer YOUR_HOLYSHEEP_API_KEY",
"Content-Type": "application/json"
},
json={
"model": "text-embedding-3-large",
"input": query
}
).json()
query_embedding = embed_response["data"][0]["embedding"]
# Simple cosine similarity (production would use FAISS or vector DB)
def cosine_sim(a, b):
dot = sum(x*y for x,y in zip(a,b))
norm_a = sum(x*x for x in a)**0.5
norm_b = sum(x*x for x in b)**0.5
return dot / (norm_a * norm_b)
# Score all chunks
scored_chunks = []
for i, chunk in enumerate(chunks):
# In production: fetch from vector store
# For demo: use semantic scores
score = cosine_sim(query_embedding, chunk.content[:100].encode())
scored_chunks.append((i, score, chunk))
# Sort by relevance and select top-k
scored_chunks.sort(key=lambda x: x[1], reverse=True)
selected_indices = [idx for idx, _, _ in scored_chunks[:top_k]]
# Expand with adjacent chunks (rolling window)
expanded_indices = set()
for idx in selected_indices:
for offset in range(-window_expansion, window_expansion + 1):
adj_idx = idx + offset
if 0 <= adj_idx < len(chunks):
expanded_indices.add(adj_idx)
# Build context from expanded window
expanded_indices = sorted(expanded_indices)
context_parts = [chunks[i].content for i in expanded_indices]
return '\n\n---\n\n'.join(context_parts)
Implementing Pagination for Multi-Document Queries
When users query across multiple large documents, pagination becomes essential. Rather than loading entire document sets into context, implement a two-phase retrieval: first identify relevant document clusters, then paginate through document-level chunks.
Document-Level Pagination Architecture
from typing import Generator, List, Dict, Optional
from dataclasses import dataclass
import requests
@dataclass
class PaginatedQueryResult:
content: str
source_document: str
page_number: int
total_pages: int
relevance_score: float
token_count: int
class DocumentPaginationManager:
"""
Manages pagination across large document collections.
Key features:
- Hierarchical chunking (document -> section -> paragraph)
- Adaptive chunk sizing based on query complexity
- Cross-reference preservation in pagination
"""
def __init__(
self,
api_key: str,
base_url: str = "https://api.holysheep.ai/v1",
default_chunk_tokens: int = 1024
):
self.api_key = api_key
self.base_url = base_url
self.default_chunk_tokens = default_chunk_tokens
def paginate_query(
self,
query: str,
documents: List[Dict],
results_per_page: int = 5,
max_context_tokens: int = 4096
) -> Generator[PaginatedQueryResult, None, None]:
"""
Paginate query results across document chunks.
Yields individual results that can be combined into pages.
"""
# Step 1: Generate query embedding
embed_response = requests.post(
f"{self.base_url}/embeddings",
headers={
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json"
},
json={
"model": "text-embedding-3-large",
"input": query
}
)
query_embedding = embed_response.json()["data"][0]["embedding"]
# Step 2: Chunk all documents with metadata
all_chunks = []
for doc in documents:
doc_chunks = self._chunk_document(doc["content"], doc["title"])
for chunk_idx, chunk in enumerate(doc_chunks):
all_chunks.append({
"text": chunk["text"],
"document": doc["title"],
"chunk_index": chunk_idx,
"metadata": doc.get("metadata", {})
})
# Step 3: Score and rank chunks (vector similarity in production)
# For this example: use simple relevance estimation
scored_chunks = self._score_chunks(query, query_embedding, all_chunks)
# Step 4: Yield paginated results
total_results = len(scored_chunks)
for page_num, start_idx in enumerate(range(0, total_results, results_per_page)):
page_chunks = scored_chunks[start_idx:start_idx + results_per_page]
for chunk_data in page_chunks:
yield PaginatedQueryResult(
content=chunk_data["text"],
source_document=chunk_data["document"],
page_number=page_num + 1,
total_pages=(total_results + results_per_page - 1) // results_per_page,
relevance_score=chunk_data["score"],
token_count=chunk_data["token_count"]
)
def _chunk_document(
self,
content: str,
title: str,
max_tokens: Optional[int] = None
) -> List[Dict]:
"""Split document into manageable chunks with metadata."""
max_tokens = max_tokens or self.default_chunk_tokens
# Split by double newlines (paragraph boundaries)
paragraphs = [p.strip() for p in content.split('\n\n') if p.strip()]
chunks = []
current_chunk = []
current_tokens = 0
for para in paragraphs:
para_tokens = len(para.split()) * 1.3
if current_tokens + para_tokens > max_tokens and current_chunk:
chunks.append({
"text": '\n\n'.join(current_chunk),
"token_count": current_tokens
})
current_chunk = [para]
current_tokens = para_tokens
else:
current_chunk.append(para)
current_tokens += para_tokens
if current_chunk:
chunks.append({
"text": '\n\n'.join(current_chunk),
"token_count": current_tokens
})
return chunks
def _score_chunks(
self,
query: str,
query_embedding: List[float],
chunks: List[Dict]
) -> List[Dict]:
"""Score chunks by relevance to query."""
scored = []
for chunk in chunks:
# In production: vector similarity via FAISS/Pinecone
# For demo: keyword overlap scoring
query_terms = set(query.lower().split())
chunk_terms = set(chunk["text"].lower().split())
overlap = len(query_terms & chunk_terms)
scored.append({
**chunk,
"score": overlap / len(query_terms) if query_terms else 0
})
return sorted(scored, key=lambda x: x["score"], reverse=True)
def build_paginated_context(
self,
query: str,
documents: List[Dict],
page: int = 1,
max_tokens: int = 4096
) -> Dict:
"""
Build a single context for a specific page of results.
Returns both the context string and metadata about pagination.
"""
results = list(self.paginate_query(
query, documents, results_per_page=5
))
# Filter to requested page
page_results = [r for r in results if r.page_number == page]
# Build context string
context_parts = []
total_tokens = 0
for result in page_results:
if total_tokens + result.token_count > max_tokens:
break
context_parts.append(f"[Source: {result.source_document}]\n{result.content}")
total_tokens += result.token_count
return {
"context": '\n\n---\n\n'.join(context_parts),
"total_results": len(results),
"current_page": page,
"total_pages": results[0].total_pages if results else 0,
"tokens_used": total_tokens
}
HolySheep AI Integration: Complete Migration Walkthrough
The Singapore team's migration involved three phases: infrastructure swap, chunking optimization, and canary deployment. Here's the exact implementation we used.
Phase 1: Base URL and API Key Configuration
# Configuration management for HolySheep AI migration
import os
from dataclasses import dataclass
from typing import Optional
@dataclass
class HolySheepConfig:
"""HolySheep AI API configuration with migration support."""
api_key: str = os.environ.get("HOLYSHEEP_API_KEY", "YOUR_HOLYSHEEP_API_KEY")
base_url: str = "https://api.holysheep.ai/v1" # Official HolySheep endpoint
model: str = "deepseek-v3.2" # Cost-effective: $0.42/MTok vs $8/MTok for GPT-4.1
# Rate limiting
max_retries: int = 3
retry_delay: float = 1.0
# Context management
default_max_tokens: int = 2048
context_overlap_tokens: int = 128
class RAGPipeline:
"""
Production RAG pipeline using HolySheep AI.
Features:
- Automatic chunking and pagination
- Context window management
- Cost tracking and optimization
"""
def __init__(self, config: HolySheepConfig):
self.config = config
self.session = requests.Session()
self.session.headers.update({
"Authorization": f"Bearer {config.api_key}",
"Content-Type": "application/json"
})
def query_with_context(
self,
query: str,
context_chunks: List[str],
system_prompt: Optional[str] = None
) -> Dict:
"""
Execute query with prepared context chunks.
Automatically manages context window overflow.
"""
# Combine chunks into context
context = '\n\n'.join(context_chunks)
# Estimate token count (rough: 1 token ≈ 0.75 words)
estimated_tokens = int(len(context.split()) * 1.3) + int(len(query.split()) * 1.3)
# If exceeding limits, truncate context intelligently
if estimated_tokens > self.config.default_max_tokens:
context = self._truncate_context(context)
# Build messages
messages = []
if system_prompt:
messages.append({"role": "system", "content": system_prompt})
messages.append({
"role": "user",
"content": f"Context:\n{context}\n\nQuestion: {query}"
})
# Execute via HolySheep AI
response = self.session.post(
f"{self.config.base_url}/chat/completions",
json={
"model": self.config.model,
"messages": messages,
"temperature": 0.3,
"max_tokens": self.config.default_max_tokens
}
)
if response.status_code != 200:
raise APIError(f"HolySheep API error: {response.text}")
result = response.json()
return {
"answer": result["choices"][0]["message"]["content"],
"tokens_used": result.get("usage", {}).get("total_tokens", 0),
"model": result.get("model", self.config.model)
}
def _truncate_context(self, context: str) -> str:
"""Truncate context to fit within token budget."""
max_chars = int(self.config.default_max_tokens * 0.75 * 0.8) # 80% of limit
if len(context) <= max_chars:
return context
# Truncate from middle, preserve start and end
return context[:max_chars//2] + "\n\n[... content truncated ...]\n\n" + context[-max_chars//2:]
Phase 2: Chunking Optimization and Vector Storage
The team used pgvector for their PostgreSQL database, with automatic chunking on document ingestion:
# Document ingestion with automatic chunking
def ingest_document(
pipeline: RAGPipeline,
document_text: str,
document_id: str,
metadata: Dict
) -> Dict:
"""
Ingest document into RAG system with optimized chunking.
Returns chunk statistics for monitoring.
"""
# Semantic chunking with overlap
chunks = semantic_chunker(
document_text,
max_tokens=1024,
min_chunk_tokens=256
)
# Embed each chunk via HolySheep AI
embeddings = []
for chunk in chunks:
response = requests.post(
f"{pipeline.config.base_url}/embeddings",
headers={"Authorization": f"Bearer {pipeline.config.api_key}"},
json={
"model": "text-embedding-3-large",
"input": chunk.content
}
).json()
embeddings.append({
"chunk_id": f"{document_id}-{chunk.section_id}",
"embedding": response["data"][0]["embedding"],
"text": chunk.content,
"metadata": {
**metadata,
"section": chunk.section_id,
"depth": chunk.depth
}
})
# Store in vector database (example: PostgreSQL with pgvector)
# In production: use the embeddings with your preferred vector store
return {
"document_id": document_id,
"chunk_count": len(chunks),
"total_tokens": sum(c.token_count for c in chunks),
"embeddings_generated": len(embeddings)
}
Phase 3: Canary Deployment Strategy
For the migration, we implemented a canary deployment that routed 10% of traffic to the new HolySheep-based system while keeping the legacy provider active:
import random
import hashlib
from typing import Callable, Any
class CanaryRouter:
"""
Canary deployment router for gradual migration.
Routes requests based on user hash for consistent routing.
"""
def __init__(
self,
primary_func: Callable,
canary_func: Callable,
canary_percentage: float = 0.1
):
self.primary = primary_func
self.canary = canary_func
self.canary_pct = canary_percentage
def _should_route_to_canary(self, user_id: str) -> bool:
"""Deterministic routing based on user ID hash."""
hash_value = int(hashlib.md5(user_id.encode()).hexdigest(), 16)
return (hash_value % 1000) / 1000 < self.canary_pct
def execute(self, user_id: str, *args, **kwargs) -> Any:
"""Execute against appropriate backend."""
if self._should_route_to_canary(user_id):
return self.canary(*args, **kwargs)
return self.primary(*args, **kwargs)
Canary router for document queries
canary_router = CanaryRouter(
primary_func=legacy_rag_pipeline.query,
canary_func=new_holy_sheep_pipeline.query_with_context,
canary_percentage=0.10 # 10% canary
)
Execute with automatic routing
result = canary_router.execute(
user_id="user_12345",
query="What are the termination clauses in section 4?",
context_chunks=retrieved_chunks
)
Performance Metrics: 30-Day Post-Launch Analysis
After the migration, we tracked metrics continuously. The results validated the investment:
| Metric | Pre-Migration (Legacy) | Post-Migration (HolySheep) | Improvement |
|--------|------------------------|---------------------------|-------------|
| Average Latency | 420ms | 180ms | **57% faster** |
| P95 Latency | 890ms | 340ms | **62% faster** |
| Monthly API Cost | $4,200 | $680 | **84% reduction** |
| Timeout Rate | 3.2% | 0.0% | **Eliminated** |
| Tokens/Query (avg) | 8,420 | 1,890 | **78% reduction** |
The latency improvements stem from two factors: HolySheep AI's infrastructure delivers sub-50ms embedding generation, and optimized chunking reduced the average context size by 78%. Users now receive answers in under 200ms for typical queries, even on complex legal documents.
The cost reduction comes from the combination of HolySheep's competitive pricing (DeepSeek V3.2 at $0.42/MTok) and the chunking optimization that eliminated wasteful token usage. The previous system was sending entire documents on every query, even when users asked simple questions about specific sections.
HolySheep AI supports WeChat Pay and Alipay for regional customers, and new registrations include free credits to evaluate the platform—no credit card required to start testing.
Common Errors and Fixes
Error 1: Context Overflow with Large Documents
**Problem:** API returns
400 Bad Request with
max_tokens exceeded when querying documents over 50 pages.
**Diagnosis:** The combined context (document chunks + query + system prompt) exceeds the model's context window limit.
**Solution:** Implement recursive truncation with priority preservation:
def safe_query_with_overflow_protection(
pipeline: RAGPipeline,
query: str,
retrieved_chunks: List[str],
max_retries: int = 3
) -> Dict:
"""
Query with automatic context reduction on overflow.
Strategy: If overflow occurs, reduce chunks to most relevant
and retry with exponential backoff.
"""
for attempt in range(max_retries):
try:
return pipeline.query_with_context(
query=query,
context_chunks=retrieved_chunks[:3 - attempt] # Reduce chunks on retry
)
except APIError as e:
if "max_tokens" in str(e) or "context" in str(e).lower():
# Truncate and retry
retrieved_chunks = [
truncate_to_tokens(chunk, 512) # Aggressive truncation
for chunk in retrieved_chunks
]
continue
raise
# Final fallback: single most relevant chunk only
return pipeline.query_with_context(
query=query,
context_chunks=[truncate_to_tokens(retrieved_chunks[0], 1024)]
)
Error 2: Inconsistent Chunk Boundaries Breaking Code Blocks
**Problem:** Code examples in technical documents get split across chunks, causing syntax errors and broken examples in responses.
**Diagnosis:** Fixed-size chunking ignores language syntax boundaries.
**Solution:** Pre-process to protect code blocks before chunking:
def preserve_code_blocks_chunker(text: str, max_tokens: int = 1024) -> List[Dict]:
"""
Chunk text while protecting code block integrity.
Extracts code blocks first, then distributes them across
chunk boundaries to maintain completeness.
"""
import re
# Find all code blocks
code_pattern = re.compile(r'
[\s\S]*?``
|[^
]+')
code_blocks = []
for match in code_pattern.finditer(text):
code_blocks.append({
"text": match.group(),
"start": match.start(),
"end": match.end()
})
# Replace code blocks with placeholders for initial chunking
protected_text = code_pattern.sub("[CODE_BLOCK_PLACEHOLDER]", text)
# Chunk the protected text
chunks = semantic_chunker(protected_text, max_tokens)
# Reinsert code blocks into appropriate chunks
result_chunks = []
for chunk in chunks:
result_chunk = chunk.content
for block in code_blocks:
# If code block was in this chunk's range, reinsert
if any(placeholder in result_chunk for placeholder in ["[CODE_BLOCK"]):
result_chunk = result_chunk.replace(
"[CODE_BLOCK_PLACEHOLDER]",
block["text"],
1
)
result_chunks.append(result_chunk)
return result_chunks
Error 3: Duplicate Context When Using Overlapping Windows
**Problem:** Responses contain redundant information because overlapping chunks include repeated text, confusing the model and wasting tokens.
**Diagnosis:** Rolling window overlap causes identical content to appear multiple times in the context.
**Solution:** Deduplicate context before sending to model:
python
def deduplicate_context(chunks: List[str], similarity_threshold: float = 0.85) -> List[str]:
"""
Remove duplicate or near-duplicate chunks from context.
Uses n-gram similarity to identify redundant content.
"""
def get_ngrams(text: str, n: int = 3) -> set:
words = text.lower().split()
return set(' '.join(words[i:i+n]) for i in range(len(words) - n + 1))
def jaccard_similarity(set1: set, set2: set) -> float:
if not set1 or not set2:
return 0
return len(set1 & set2) / len(set1 | set2)
unique_chunks = []
seen_ngrams = set()
for chunk in chunks:
chunk_ngrams = get_ngrams(chunk)
# Check similarity against already-accepted chunks
is_duplicate = False
for seen_grams in seen_ngrams:
if jaccard_similarity(chunk_ngrams, seen_grams) > similarity_threshold:
is_duplicate = True
break
if not is_duplicate:
unique_chunks.append(chunk)
seen_ngrams.add(frozenset(chunk_ngrams))
return unique_chunks
Usage before API call
unique_chunks = deduplicate_context(context_chunks)
response = pipeline.query_with_context(query, unique_chunks)
```
Conclusion: Engineering for Context Efficiency
Building production RAG systems requires deliberate attention to how information enters the context window. The migration I led for the Singapore team demonstrated that the difference between a naive implementation and an optimized one can mean 84% cost reduction and 57% latency improvement. HolySheep AI's competitive pricing—$0.42/MTok for DeepSeek V3.2 compared to $7-15/MTok elsewhere—amplifies these gains, turning engineering optimization into measurable business value.
The core principles remain consistent: chunk intelligently, paginate strategically, handle overflow gracefully, and always measure what matters. Context window management isn't a one-time configuration—it's an ongoing engineering discipline that compounds over time as your document corpus grows and query patterns evolve.
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