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.

Related jobs
Jobs that call for the skills explored in this talk.

Machine Learning Engineer

Picnic Technologies B.V.

Picnic Technologies B.V.
Amsterdam, Netherlands

Intermediate
Senior
Python
Structured Query Language (SQL)
+1

Matching moments

Prompt injection as an unsolved AI security problem
07:39 MIN

Prompt injection as an unsolved AI security problem

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Unlocking LLM potential with creative prompting techniques
04:59 MIN

Unlocking LLM potential with creative prompting techniques

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Building and iterating on an LLM-powered product
05:03 MIN

Building and iterating on an LLM-powered product

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The hardware requirements for running LLMs locally
03:55 MIN

The hardware requirements for running LLMs locally

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The evolving role of the machine learning engineer
02:20 MIN

The evolving role of the machine learning engineer

AI in the Open and in Browsers - Tarek Ziadé

Increasing the value of talk recordings post-event
04:57 MIN

Increasing the value of talk recordings post-event

Cat Herding with Lions and Tigers - Christian Heilmann

Using AI to overcome challenges in systems programming
02:49 MIN

Using AI to overcome challenges in systems programming

AI in the Open and in Browsers - Tarek Ziadé

Writing authentic content in the age of LLMs
07:43 MIN

Writing authentic content in the age of LLMs

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