AI Lesson & Submodule
Prompt Trimming & Memory
Implement sliding windows, summarization memory, and truncation logics.
Why This Matters
Trimming context intelligently retains semantic history without wasting API costs on redundant text.
Deep-Dive Explanation
To prevent history from exhausting the context window, several strategies can be employed. Sliding Window Truncation discards the oldest messages when the token count exceeds a threshold. Recursive Summarization uses a smaller LLM in the background to summarize older turns into a compact summary paragraph, which is appended to the system prompt, preserving history themes in few tokens.
What You Will Learn
- •Building a sliding window history trimmer
- •Using model summaries as memory buffers
- •Trimming older conversation turns based on token limits
Concepts Covered
Sliding Window HistorySummarized History MemoryToken Truncation Logic
Mapped Foundation Project: Context Window Dashboard
Diagnostic analyzer tracking chat history expansion, system prompt parameters, and memory optimization suggestions.
Architecture Preview
A dashboard showing total token allocation, system overhead, and dynamic chat history truncation sliders.
Chat History InputHistory Truncator ModelToken Count Calculator
Tech Stack Planned
Next.jsTypeScriptTailwind CSS