Optimize conversation history, manage token limits, and reduce costs
Every conversation with an AI model has a memory — the context window. Managing this context effectively is critical for performance, cost, and user experience. Too little context and the AI loses track of the conversation. Too much and you hit token limits and exponentially increasing costs.
The context window is the maximum number of tokens (words, parts of words, and characters) that a model can process in a single request — including both input and output. It's the AI's "working memory."
Important: Tokens count for BOTH input (your prompt + conversation history) AND output (the AI's response). If you have 2,000 tokens of history and request a 1,000-token response, you need a 3,000+ token context window.
Included in Context:
Context Grows With:
Hard Limit Exceeded:
The API returns an error and refuses to process the request. Your application breaks or shows an error message to the user.
Approaching Limit:
The model may truncate responses mid-sentence or lose earlier context, leading to degraded conversation quality.
Keep only the most recent N messages in context, discarding older ones.
Keep last 10 messages
→ Discard message 1
→ Add new message 11
Pros: Simple, predictable memory usage
Cons: Loses older important context
Periodically summarize older messages to compress history.
Messages 1-20: [Summary]
Messages 21-30: [Full text]
Pros: Retains key information
Cons: Loses details, requires summarization calls
Keep important messages (flagged by user or system) and discard less relevant ones.
✓ System prompt
✓ Key decisions
✗ Small talk
✓ Recent messages
Pros: Keeps what matters most
Cons: Requires logic to classify importance
Clear context and start fresh when changing topics or reaching limits.
"Starting new conversation"
→ Clear all history
→ Keep system prompt only
Pros: Clean slate, predictable behavior
Cons: Loses all conversation continuity
Most AI APIs charge per token processed. Managing context directly impacts your costs.
10K
tokens in history
$0.01/request @ GPT-4
100K
requests/month
= $1,000/month
50%
reduction
= $500 saved
System prompts are sent with EVERY request. Make them concise.
Wasteful (150 tokens):
"You are a helpful assistant designed to provide customer support. Always be polite, professional, and courteous..."
Efficient (30 tokens):
"Professional customer support agent. Be helpful, concise."
Don't use GPT-4 for basic classification. GPT-3.5 costs ~10x less.
GPT-4: Complex reasoning, analysis, creativity
GPT-3.5: Classification, simple Q&A, data extraction
Process multiple items in a single request instead of separate calls.
Instead of: 10 requests × 500 tokens = 5,000 tokens
Do: 1 request × 1,200 tokens = 1,200 tokens (76% savings)
Store and reuse answers to frequently asked questions instead of calling the API every time.
Prevent runaway costs by capping response length to only what's needed.
Set max_tokens=150 for short answers, max_tokens=500 for detailed responses
Track token consumption per request to identify optimization opportunities
Implement context window caps to prevent API errors and runaway costs
Match model capabilities to task complexity — don't overpay for simple tasks
Let users know when context is cleared to avoid confusion
Experiment with sliding windows, summarization, and selective retention
Cost optimization becomes critical at 1000+ requests/day
We can help you design efficient context management strategies that balance performance and cost