Daniel Töws

Using LLMs in your Product

What if your LLM could call your application's code? Learn how function calling connects models to proprietary data and transforms them into intelligent agents.

Using LLMs in your Product
#1about 2 minutes

Three pillars for integrating LLMs in products

The talk will cover three key areas for product integration: using the API, mastering prompt engineering, and implementing function calls for external data.

#2about 3 minutes

Making your first OpenAI API chat completion call

This section covers the basic code structure for making a chat completion request, including the different message roles and the stateless nature of the API.

#3about 3 minutes

Choosing the right LLM for your use case

Key factors for selecting a model include its training dataset cutoff date, context length measured in tokens, and the number of model parameters.

#4about 5 minutes

Best practices for effective prompt engineering

Improve LLM outputs by writing clear instructions, providing context with personas and references, and breaking down complex tasks into smaller steps.

#5about 4 minutes

Understanding and defending against prompt injection

Prevent users from bypassing system instructions by reinforcing the original rules with a post-prompt at the end of the message history.

#6about 4 minutes

Giving LLMs new abilities with function calling

Function calling allows the LLM to request help from your own code to access external data or perform actions like searching a database.

#7about 2 minutes

Summary and resources for further learning

The talk concludes with a recap of core concepts and provides resources for advanced prompting techniques and retrieval-augmented generation (RAG).

#8about 7 minutes

Audience Q&A on practical LLM implementation

The Q&A covers practical concerns like managing context length, prompt testing costs, implementing function call logic, and ensuring reliable JSON output.

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