Day 4 : LLM Token Waste: The Problem Nobody Talks About

Description

How to Reduce LLM Costs and Latency: Advanced Token Optimisation Guide In this video, we dive deep into advanced strategies to reduce token usage, directly impacting your project's cost-efficiency and response latency. Whether you are working with Gemini, PaLM, or other instruction-tuned models, these techniques will help you build leaner, faster AI workflows. What you will learn: • Prompt Precision: Learn why being direct and avoiding verbose instructions is critical for efficiency. • The Art of Trimming: Discover how to remove "filler" words and stop words without losing meaning. • Smart Context Management: Why passing raw JSON or full documents is a mistake, and how to use key-value pairs or summaries instead. • Zero-Shot vs Few-Shot: Understand why models like Gemini often perform better with fewer examples, saving you significant token counts. • Output Control: Techniques to force the model into concise responses, such as one-line JSON or strict word limits. • RAG Pipeline Tuning: How to use smart chunking (300–500 tokens) and vector search filtering to ensure only the most relevant data is processed. • Developer Tools: A look at using Python token counters and API dashboards to monitor and estimate usage before you hit "send". • Pre-filtering with Embeddings: Using classification or embeddings to filter data so you only send what is absolutely necessary to the LLM. Key Takeaway: Optimising tokens is not just about saving money; it is about improving the user experience by decreasing the time it takes for your model to respond. Resources Mentioned: • Python transformers library for token counting. • Google API dashboards for usage monitoring. #AI #MachineLearning #LLM #GenerativeAI #TokenOptimisation #GeminiAI #AIDevelopment #RAG In this video, we dive deep into advanced strategies to reduce token usage, directly impacting your project's cost-efficiency and response latency. Whether you are working with Gemini, PaLM, or other instruction-tuned models, these techniques will help you build leaner, faster AI workflows. What you will learn: • Prompt Precision: Learn why being direct and avoiding verbose instructions is critical for efficiency. • The Art of Trimming: Discover how to remove "filler" words and stop words without losing meaning. • Smart Context Management: Why passing raw JSON or full documents is a mistake, and how to use key-value pairs or summaries instead. • Zero-Shot vs Few-Shot: Understand why models like Gemini often perform better with fewer examples, saving you significant token counts. • Output Control: Techniques to force the model into concise responses, such as one-line JSON or strict word limits. • RAG Pipeline Tuning: How to use smart chunking (300–500 tokens) and vector search filtering to ensure only the most relevant data is processed. • Developer Tools: A look at using Python token counters and API dashboards to monitor and estimate usage before you hit "send". • Pre-filtering with Embeddings: Using classification or embeddings to filter data so you only send what is absolutely necessary to the LLM. Key Takeaway: Optimising tokens is not just about saving money; it is about improving the user experience by decreasing the time it takes for your model to respond. Resources Mentioned: • Python transformers library for token counting. • Google API dashboards for usage monitoring. #AI #MachineLearning #LLM #GenerativeAI #TokenOptimisation #GeminiAI #AIDevelopment #RAG