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World Congress 2024 • Aug 29, 2024 • Session details

Using LLMs in your Product

Daniel Töws

Integrating LLMs means shifting from rigid code to probabilistic workflows. Master structured prompt engineering, mitigate prompt injections, and leverage RAG to build secure, dynamic AI features.

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#1 about 2 min

Three pillars of integrating large language models into products

Developers can leverage modern generative AI frameworks through direct API integrations, prompt design, and function callbacks.

#2 about 7 min

Utilizing chat completion requests and managing context length

Sending full chat histories to stateless APIs requires understanding token limits and prioritizing models based on dataset size and recency.

#3 about 6 min

Applying prompt engineering best practices for reliable model outputs

Providing precise instructions with contextual personas and product references reduces ambiguity and minimizes factual hallucinations.

#4 about 5 min

Designing system messages and mitigating prompt injection security risks

Establishing rigid system messages helps prevent malicious user inputs from hijacking the intended conversation scope.

#5 about 5 min

Enhancing language models with backend logic using function calls

Exposing custom callback functions allows text generation models to retrieve real-time data or trigger external system actions.

#6 about 2 min

Exploring retrieval augmented generation and advanced prompting techniques

Implementing retrieval augmented generation provides a mechanism to query massive localized datasets without fine-tuning underlying models.

#7 about 7 min

Addressing context limits inference costs and reliable json generation

Managing persistent connection lifecycles involves summarizing historical data logs and validating structured outputs using explicit tool boundaries.

Matching moments

4:56 min

Building agentic workflows using prompt engineering and language models

2:45 min

Evolving traditional coding logic into LLM prompting

Ankit Patel Ankit Patel · WWC 2024

1:12 min

Enhancing conversational intent through modern large language models

Nathaniel Okenwa Nathaniel Okenwa · WWC 2025

2:44 min

Understanding language models and autonomous executing agents

Chris Heilmann +2 · LIVE

1:31 min

The shift from human prompt engineering to AI-generated prompts

Vitaly Friedman Vitaly Friedman · WWC Europe 2026

2:05 min

Enhancing language models with retrieval-augmented generation

Mary Grygleski Mary Grygleski · LIVE

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