Description:

Large Language Models(LLMs) are good at reasoning based on their knowledge. This talk explores how you can use the power of LLMs to add intelligence like coding assistants, text-to-sequel, etc to existing applications.

One of the simplest ways to start adding intelligence is by using an LLM with fine-tuned prompts. You can find the answers to questions like: - What are some of the things that you need to consider while prompt engineering? - What are the limits of prompt engineering?

After finding out the limits of prompt engineering, let us understand how to augment the knowledge of the LLM using vector databases. You can learn things like: - Ingesting the data into the vector databases. - Considerations in data ingestion to improve the LLM performance.

We will also cover the concept of AI agents that given a set of capabilities or tools can figure out how to use them where relevant in an intelligent fashion. You can learn - How do agents work? - Where are they useful?

After this talk, you will learn how to add intelligence to existing applications with the help of the ever-popular LLMs using open-source frameworks.

Prerequisites:

Knowledge of Python fundamentals

Content URLs:

Example Source Repo

Speaker Info:

Nithish Raghunandanan is a Developer Advocate at Couchbase who loves to build products that solve real-world problems in short spans of time. He has experienced different areas of the industry having worked in diverse companies in Germany and India. Apart from work, he likes to travel and interact and engage with the tech community through Meetups & Hackathons. In his free time, he likes to try stuff out by hacking things together.

Speaker Links:

Personal Website LinkedIn Github