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World Congress 2024 • Aug 22, 2024 • Session details

Lessons Learned Building a GenAI Powered App

Mete Atamel

What happens when you replace deterministic APIs with unpredictable LLMs? Discover the defensive engineering tactics needed to tame generative AI and build reliable, production-ready workflows.

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#1 about 5 min

Evolving a static app into a generative AI application

How limitations in a rigid trivia app motivated the integration of generative AI.

#2 about 5 min

Demonstrating a real-time generative AI quiz application

A live demonstration of generating custom trivia topics using different language models.

#3 about 2 min

Architecture of a multi-platform generative AI app

Designing a system utilizing Flutter for the frontend, Cloud Run for hosting, and Firestore for storage.

#4 about 2 min

Experimenting with generative models in Vertex AI

Using the prompt gallery to test multiple models and easily extract deployment code.

#5 about 2 min

Writing effective prompts for quiz data generation

How providing clear expectations and formatting constraints yields reliable trivia datasets.

#6 about 1 min

Implementing image generation using different Imagen versions

Transitioning across models to produce relevant quiz imagery through simplified API parameters.

#7 about 4 min

Handling inconsistent and non-deterministic LLM outputs

Preparing backend logic to manage shifting output responses despite identical configurations.

#8 about 2 min

Deciding if large language models are actually necessary

Exploring traditional solutions like fuzzy string matching before defaulting to AI implementations.

#9 about 2 min

Best practices for versioning and managing prompts

Why starting with simple instructions and treating prompts as versioned code prevents regressions.

#10 about 2 min

Defensive coding techniques for unpredictable LLM responses

Implementing retry and validation layers to handle malformed structures or empty responses.

#11 about 1 min

Pinning model versions for stable application behavior

Preventing unexpected breakages by linking system integrations to exact iteration subsets.

#12 about 2 min

Unifying backend integrations with the LangChain framework

Streamlining multiple disparate model libraries into a single integration toolchain.

#13 about 1 min

Applying software engineering principles to optimize LLMs

Reducing latency and usage costs through parallel calls, batched requests, and caching.

#14 about 2 min

Validating structural integrity of generated data formats

Checking expected shapes and structures over subjective output quality to maintain reliability.

#15 about 3 min

Evaluating LLM response accuracy using secondary LLMs

Creating an automated validation loop where a secondary model grades the initial facts.

#16 about 3 min

Grounding language models with external search and custom data

Increasing output relevancy by linking model generation requests to external knowledge sources.

Matching moments

7:28 min

Accelerating product features using generative large language models

David Singleton David Singleton +1 · Coffee With Developers

1:28 min

Core challenges facing the generative AI developer ecosystem today

Prashanth Chandrasekar Prashanth Chandrasekar · World Congress 2024

2:20 min

Integrating generative AI into software development workflows

Chris Wysopal Chris Wysopal · World Congress 2024

5:18 min

Generating complete web applications using language model prompts

Chris Heilmann Chris Heilmann +2 · LIVE

3:13 min

Integrating state-of-the-art generative models and automated processing

Kapil Gupta Kapil Gupta · World Congress 2025

3:36 min

Overview of generative AI and the presentation agenda

juarezjunior juarezjunior · World Congress 2024

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