Building Intelligent Chatbots | Key Challenges in Building Enterprise LLM Applications

As enterprises move beyond ChatGPT, Bard, and 'demo applications' of large language models, product leaders, and engineers are running into challenges. The magical experience we observe on content generation and summarization tasks using ChatGPT is not replicated on custom LLM applications built on enterprise data. This frustration can lead to one (or a mix) of the following reactions:
- Enterprise LLMs are yet another hype or fad at best.
- Enterprise LLMs are good only for summarization and content generation tasks; They are not suitable for enterprise applications.
- Building LLM applications is significantly harder than just using an off-the-shelf foundation model.
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The third point is the closest to reality. Enterprise LLM applications are easy to imagine and build a demo out of, but somewhat challenging to turn into a business application. The complexity of datasets, training costs, cost of token usage, response latency, context limit, fragility of prompts, and repeatability are some of the problems faced during product development.
As the Chief Data Scientist at Data Science Dojo, Raja has led a team of extremely talented data scientists and software engineers to deliver multiple enterprise applications of large language models. In this live talk, Raja will go over the challenges faced by his team while building these mission-critical applications and how the team overcame those challenges.
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Table of Contents:
0:00 - Introduction
1:14 - Agenda - Challenges of Large and Complex Datasets
1:27 - Misaligned Behavior of Model with Human Values
10:15 - Increase Context Length to Understand
13:57 - How to Keep Costs Low
21:12 - Latency - Responsible LLM Applications
31:46 - Experiment Design and Reproducibility Issue
34:37 - How to Evaluate an LLM
41:33 - QnA
#largelanguagemodel #llm #generativeai #llmapplications

[@Datasciencedojo]

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[@Leesen]

Hello. Is it possible to get a copy of these slides? Thank you