Choose Your Level
Pick the difficulty that matches where you are. You can come back and try a harder level later.
Summarise a Massive Document
Write a prompt to distil a 50-page PDF into a 1-page executive summary.
Design a Rolling Summary Flow
Solve the context limit problem for a 3-hour podcast transcript.
Pass the Needle-in-a-Haystack Test
Force an LLM to find one specific clause hidden in a 100-page contract.
Context Window Management & Token Optimization
Context windows limit the amount of text an LLM can process in a single request. AI developers demonstrate performance optimization by tracking BPE token counts (tiktoken), mitigating 'Lost in the Middle' attention loss, implementing context window compression, and optimizing API token costs.
1. Context Window Allocation & Token Budget Model
Token budget specification allocating exact token limits for system prompts, retrieved context, chat history, and response completion.
2. Dynamic Prompt Compression & Truncation Script
Python script using sliding window context summarization and token truncation to stay within API limits.
3. LLM API Token Cost Optimization Report
Financial optimization report measuring cost savings achieved through prompt compression and cache hits.
Frequently Asked Questions (Context Window Management)
What is the 'Lost in the Middle' phenomenon in LLM context windows?
Research shows LLMs recall information best at the very beginning and very end of long context windows, while frequently missing key details buried in the middle.
How do Byte-Pair Encoding (BPE) tokenizers count text?
BPE tokenizers break words into common sub-word character chunks (e.g. 'unbelievable' -> 'un', 'believ', 'able'), where 1 token averages ~4 characters or 0.75 words in English.
How do you manage conversation history in long multi-turn chat sessions?
Use a sliding window memory approach (keep last N turns) paired with periodic background LLM summarization of older chat history.
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