
This podcast was created using BeFreed's AI, based on selected books, the creator's learning goals, and their preferred tone.






This lesson is part of the learning plan: 'Architecting High-Stakes AI Systems'. Lesson topic: Tokens and Context Window Management Overview: Long AI conversations often lead to lost instructions. Learn how token mechanics and context windows work to condense prompts for better accuracy. Key insights to cover in order: 1. Tokens represent word fragments rather than whole words, directly impacting both the cost and the input limits of prompts. 2. Large language models possess a finite context window that requires strategic condensation of long inputs to maintain memory. 3. Condensing prompts before entry prevents the model from losing track of critical instructions during long-form professional conversations. Listener profile: - Learning goal: Learn prompt engineering for work projects to become expert level - Background knowledge: I have used ChatGPT for personal tasks and have experience with few-shot learning techniques. - Guidance: Build on existing ChatGPT experience and few-shot knowledge. Focus on professional applications and advanced techniques for work contexts. Tailor examples, pacing, and depth to this listener. Avoid analogies or references that assume knowledge outside this listener's profile.

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From Columbia University alumni built in San Francisco
