Beyond the Hype: A Realistic Look at Large Language Models • Jodie Burchell • GOTO 2024

Large Language Models (LLMs) are not showing signs of Artificial General Intelligence (AGI) - they are sophisticated pattern matching systems with specific limitations

LLMs are best suited for natural language processing tasks like text classification, summarization, translation, and question answering within their training domain

The success of modern LLMs is built on three key advances:

• Development of transformer architecture
• Availability of massive training datasets (Common Crawl)
• GPU acceleration through CUDA

Retrieval Augmented Generation (RAG) helps overcome LLM limitations by:

• Augmenting responses with external knowledge
• Reducing hallucinations
• Enabling domain-specific applications

Key challenges when deploying LLMs:

• Choosing the right model for specific use cases
• Proper tuning and configuration
• Managing computing requirements
• Handling sensitive data

LLM performance should be evaluated based on:

• Ability to generalize to new problems
• Performance within intended problem domain
• Specific benchmarks for target tasks

Common misconceptions about LLMs:

• They don’t truly understand context
• They can’t create new knowledge
• They’re not replacing human intelligence
• They require quality training data

Best practices for LLM implementation:

• Define clear scope and use cases
• Measure performance carefully
• Use domain-specific fine-tuning when needed
• Implement proper validation and testing

Technical considerations for RAG:

• Chunk size and overlap
• Embedding model selection
• Number of retrieved chunks
• Vector database configuration