Improving Generative AI for Code: Techniques for Context Awareness, Testing, and Multi-Agent Systems

Developers who have worked with generative AI for coding often find its potential impressive, but it's clear that code generation alone isn't always enough. Without enhancements like context awareness, the generated code can miss critical project-specific details or best practices.

In this webinar, we’ll dive into how to leverage Large Language Models (LLMs) for building applications that meet exact specifications. We’ll cover essential techniques for integrating additional layers into LLMs to ensure code integrity and effectiveness in large-scale environments.

Key topics include:
- Contextual Awareness: Techniques for local code indexing and advanced Retrieval-Augmented Generation (RAG) to improve contextual relevance in extensive codebases.

- Test-Driven Flow Engineering: Leveraging the Generative Adversarial Network (GAN) framework to iteratively generate, test, and refine code.

- Comprehensive Testing and Review: Strategies for optimizing test automation and maintaining clean, healthy code.

- Multi-Agent Systems: Implementing specialized agents and fine-tuned models for specific tasks in the development pipeline.

[yuli.kamakura]

Excuse me, where did you get this data?：
"""
+39%
More churned code
(after two weeks)
54%
Of Al generated code is incorrect
or partially incorrect*
"""