Archetypes of LLM apps

The article discusses the author's presentation on how startups are utilizing AI, specifically Language Learning Models (LLMs), to innovate and disrupt various industries. The author outlines the core techniques, architectures, and approaches shaping the next generation of products. The presentation is divided into three parts: building blocks, basic applications, and advanced applications. Building blocks include chat, embeddings, semantic search, fine-tuning, and other tools like moderation, voice, images, and batch processing. Basic applications of LLMs include code generation, text to SQL, summarization, advanced moderation, text generation, analysis, intent detection, and data labeling. Advanced applications focus on complex techniques like retrieval-augmented generation (RAG), agents, and swarms. The author emphasizes that AI is no longer a future tool in software development but is already the standard.

Key takeaways

The author, Philip, shares his presentation on how startups are leveraging AI, specifically Language Learning Models (LLMs), to disrupt various industries.
LLMs are being used in a variety of applications, from code generation and text summarization to intent detection and data labeling, transforming the way businesses operate.
Advanced applications of LLMs, such as retrieval-augmented generation (RAG), are emerging, pushing the boundaries of what AI can achieve.
Despite the benefits, there are significant costs and complexities associated with implementing LLMs, including the need for substantial infrastructure and the potential for high costs when processing large volumes of data.

Archetypes of LLM apps

Key takeaways

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