The hottest Substack posts of Retrieve and Generate

And their main takeaways

Self-Improving AI Coding Agents in 5 Minutes

9 implied HN points • 26 Jun 25
🕹 Technology AI Software Coding Automation Development
  1. AI coding agents can struggle with tasks and make mistakes. It's not just the AI's fault; many parts of the system can contribute to these errors.
  2. You can help your AI coding agent improve itself by capturing its logs, asking it to find errors, and fixing those issues. This process can make the agent more reliable and faster.
  3. Running specific benchmarks regularly can help track your AI's performance over time. This way, you can spot any problems early and improve the system continuously.

Beware Tunnel Vision in AI Retrieval

41 implied HN points • 19 Jun 23
  1. When architecting products with large language models, consider various retrieval systems, not limited to vector search.
  2. Evaluate the nature of your data and queries to choose the most suitable retrieval method, which could be vector search, keyword search, or others.
  3. Start with keyword search for high precision and consider using vector search as a fallback or for increased recall.

With LLMs, Enterprise Data is Different

19 HN points • 19 Jul 23
  1. Enterprise data for Large Language Models (LLMs) is different than consumer data.
  2. Enterprise LLM projects involve closed-domain data, larger data sizes, and multiple data modalities.
  3. Understanding the unique characteristics of enterprise data is crucial for the success of LLM projects in business settings.

The 5 Use Cases for Enterprise LLMs

3 HN points • 17 Sep 23
  1. Choosing the right use case is crucial for the success of an Enterprise LLM project.
  2. LLMs offer capabilities like instruction following, natural language fluency, and memorized knowledge.
  3. Use case categories for Enterprise LLMs include data transformations, natural language interfaces, workflow automations, copilots, and autonomous agents.