95% Accurate LLM Agents | Shocking or Myth

In this video, we are going to debunk if an LLM Agent was really able to get to a 95% accurate Level.

In this video, we'll dive into a real-world case study where a Fortune 500 company used Lamini Memory Tuning to slash hallucinations and create a 94.7% accurate LLM agent for SQL queries.
We'll explore the challenges they faced, the limitations of traditional approaches, and the revolutionary impact of Lamini Memory Tuning. You'll learn:

The problem: Why traditional fine-tuning methods often struggle with complex data schemas.

Lamini's solution: How Lamini Memory Tuning leverages a unique approach to achieve remarkable accuracy.

Step-by-step walkthrough: We'll walk you through the entire process from diagnosis to implementation.

Beyond SQL: Learn how this approach can be applied to other code-based LLM applications.

Don't miss this opportunity to see how Lamini is transforming the world of enterprise LLMs!


#lamini #95percentaccuracy #rag #bestllms #promptengineer48
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[anujyotisonowal]

Just wow ... amazing video.. keep it up 👍

[sitedev]

50% accuracy for RAG seems a bit pessimistic. I build RAG systems for my clients and always achieve way better than 50% by simply ensuring I am not ingesting rubbish data by reviewing and preprocessing it (distilling) prior to ingestion.

[samyio]

Yesterday i programmed this but without finetuning. Its so crazy, how you can predict the next inventions these days. Do you think i can get a 1B model to reliable function calling without finetuning?

[publicsectordirect]

Great video! Is there any info on the licence type?