Simon A.T. Jiménez

Three years of putting LLMs into Software - Lessons learned

What if LLMs are just a new, unreliable kind of API? Learn the critical lessons from three years of building real-world software with them.

Three years of putting LLMs into Software - Lessons learned
#1about 4 minutes

Understanding the fundamental nature of LLMs

LLMs are unreliable pattern matchers that appear intelligent but lack true understanding, requiring developers to manage context and anticipate failures.

#2about 4 minutes

Controlling LLM output with API parameters

API parameters like temperature and top_p allow for control over the determinism and creativity of LLM responses by manipulating token selection probabilities.

#3about 7 minutes

Viewing LLMs as a new kind of API

LLMs should be treated as a new type of API for text manipulation, not as intelligent agents, because they are advanced pattern matchers with significant limitations.

#4about 5 minutes

Implementing practical LLM use cases in software

LLMs can be used for tasks like audio transcription, image analysis for OCR, and text reformulation by providing clear instructions and examples in the prompt.

#5about 4 minutes

Navigating legal compliance and data privacy

Using paid APIs with data privacy contracts, implementing human-in-the-loop workflows, and understanding the European AI Act are crucial for legal compliance.

#6about 2 minutes

Understanding the security risks of AI integrations

Integrating LLMs with external APIs or internal data creates significant security risks like prompt injection, requiring careful control over the AI's permissions and actions.

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

Matching moments

Three pillars for integrating LLMs in products
01:47 MIN

Three pillars for integrating LLMs in products

Using LLMs in your Product

Why natural voice AI has been so difficult
04:47 MIN

Why natural voice AI has been so difficult

Hello JARVIS - Building Voice Interfaces for Your LLMS

Addressing the core challenges of large language models
05:18 MIN

Addressing the core challenges of large language models

Accelerating GenAI Development: Harnessing Astra DB Vector Store and Langflow for LLM-Powered Apps

Addressing the key challenges of large language models
02:55 MIN

Addressing the key challenges of large language models

Large Language Models ❤️ Knowledge Graphs

Demonstrating LLM hallucinations with tricky questions
06:55 MIN

Demonstrating LLM hallucinations with tricky questions

Give Your LLMs a Left Brain

Why you need to prompt large language models like a child
02:33 MIN

Why you need to prompt large language models like a child

Developers vs Scammers, Bad Design, AI is Pointless, AJAX is 20 and more - The Best of LIVE 2025 - Part 1

The rapid evolution and adoption of LLMs
02:35 MIN

The rapid evolution and adoption of LLMs

Building Blocks of RAG: From Understanding to Implementation

Using large language models as a learning tool
03:42 MIN

Using large language models as a learning tool

Google Gemini: Open Source and Deep Thinking Models - Sam Witteveen

Featured Partners

Related Videos

See all videos
Using LLMs in your Product
31:12

Using LLMs in your Product

Daniel Töws

Inside the Mind of an LLM
27:11

Inside the Mind of an LLM

Emanuele Fabbiani

Bringing the power of AI to your application.
32:26

Bringing the power of AI to your application.

Krzysztof Cieślak

Hello JARVIS - Building Voice Interfaces for Your LLMS
28:13

Hello JARVIS - Building Voice Interfaces for Your LLMS

Nathaniel Okenwa

AI: Superhero or Supervillain? How and Why with Scott Hanselman
25:17

AI: Superhero or Supervillain? How and Why with Scott Hanselman

Scott Hanselman

You are not my model anymore - understanding LLM model behavior
24:11

You are not my model anymore - understanding LLM model behavior

Andreas Erben

Self-Hosted LLMs: From Zero to Inference
30:04

Self-Hosted LLMs: From Zero to Inference

Roberto Carratalá & Cedric Clyburn

Exploring LLMs across clouds
28:38

Exploring LLMs across clouds

Tomislav Tipurić

Related Articles

View all articles
Daniel Cranney
Stephan Gillich - Bringing AI Everywhere
In the ever-evolving world of technology, AI continues to be the frontier for innovation and transformation. Stephan Gillich, from the AI Center of Excellence at Intel, dove into the subject in a recent session titled "Bringing AI Everywhere," sheddi...
Stephan Gillich - Bringing AI Everywhere

From learning to earning

Jobs that call for the skills explored in this talk.