GenAI and LLMs development, trends and implications (30.9. - 6.10.2024)

Adoption:

• Generative AI will require 80% of engineering workforce to upskill through 2027
• Demand is growing for robust computing - hyperscale data centers ignite Construction Surge
• Knowledge-as-a-service - the future of community business models
• Generally AI - generative AI and creativity
• When generative AI isn’t always the answer

LLMs generally:

• What to consider when adopting Large Language Models
• Choosing the architecture for your GenAI application
• Evaluating performance of LLM-based applications
• Do we really need more complex models?

AI agents:

• What makes a true AI agent? Rethinking the pursuit of autonomy
• The intersection of tool calling and reasoning in Generative AI

RAG:

• Chunking strategies - why, when, and how to chunk for enhanced RAG
• Improving RAG quality with knowledge graphs
• Stop guessing and measure your RAG system to drive real improvements
• Using HyDE technique for better LLM RAG retrieval
• Build a RAG-powered chatbot With Gemini, MyScaleDB

AI-enhanced software development:

• The AI-native developers who build software through prompt engineering
• Using Spring AI with LLMs to generate Java tests
• Legacy codebases modernization meets GenAI
• The state of AI completion in JetBrains IDEs - complete the un-completable
• PyCharm - prompting AI directly in the editor
• Comparison of AI coding assistants - Codeium versus Copilot

Enhancing applications with GenAI:

• Spring Petclinic - Implementing an AI assistant with Spring AI
• Supercharging your AI applications with Spring AI Advisors
• ChatClient Fluent API in Spring AI
• OpenAI releases stable version of .NET Library with GPT-4o Support