The hottest Substack posts of AI Encoder: Parsing Signal from Hype

And their main takeaways

GraphRAG Analysis, Part 1: How Indexing Elevates Knowledge Graph Performance in RAG

70 HN points • 09 Jul 24
🕹 Technology Analysis Research Evaluation Modeling Metrics
  1. Knowledge graphs do not significantly impact context retrieval in RAG, as all methods showed similar context relevancy scores.
  2. Neo4j with its own index improved answer relevancy and faithfulness compared to Neo4j without indexing and FAISS, showcasing the importance of effective indexing for precise content retrieval in RAG applications.
  3. Developers need to consider the trade-offs between ROI constraints and performance improvements when deciding to use GraphRAG, especially in high-precision applications that require accurate answers.

Langchain’s built-in eval metrics for AI output: how are they different?

0 implied HN points • 22 May 24
🕹 Technology AI Evaluation Metrics Machine Learning Bias
  1. Users prefer coherent responses over detailed ones for helpfulness, highlighting the importance of logical structuring in AI output.
  2. Controversial content can be associated with criminality, suggesting that engaging material may overlap with unlawful topics.
  3. Bias from model choices, like using GPT-3.5 Turbo, can impact metric correlations, emphasizing the need for acknowledging biases in AI evaluation.

Gemini 1.5 Pro preview hack

0 implied HN points • 22 Apr 24
🕹 Technology AI Programming
  1. Gemini 1.5 Pro without API access is limited to AI Studio with a temperature setting constraint of 1.
  2. A quick hack involves modifying the system prompt to gain more control.
  3. Consider subscribing to AI Dev Hacks for more tips and support.

How do you know if your RAG/fine-tuned LLM implementation is good?

0 implied HN points • 20 Sep 23
🕹 Technology AI Deep Learning Testing Metrics Tool
  1. To know if your RAG/fine-tuned LLM implementation is good, set up custom test cases that match your use case for evaluation.
  2. Utilize tools like DeepEval for defining custom test cases and metrics to assess your AI model's strengths and weaknesses.
  3. Before introducing an AI model to production data, rigorously test and evaluate its performance with various tests to ensure reliability.