The hottest Information Retrieval Substack posts right now

And their main takeaways
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Top Technology Topics

There's more to LLMs than chat

Generating Conversation • 216 implied HN points • 15 Feb 24
🕹 Technology AI UX Design Information Retrieval Human-computer interaction
  1. Chat interfaces have limitations, and using LLMs in more diverse ways beyond chat is essential for product innovation.
  2. Chat-based interactions lack the expression of uncertainty, unlike other search-based approaches, which impacts user trust in the information provided by LLMs.
  3. LLMs can be utilized to proactively surface information relevant to users, showing that chat isn't always the most effective approach for certain interactions.

Advanced Prompt Engineering

Deep (Learning) Focus • 609 implied HN points • 08 May 23
🕹 Technology AI/ML Deep Learning Prompt engineering Information Retrieval
  1. LLMs can solve complex problems by breaking them into smaller parts or steps using CoT prompting.
  2. Automatic prompt engineering techniques, like gradient-based search, provide a way to optimize language model prompts based on data.
  3. Simple techniques like self-consistency and generated knowledge can be powerful for improving LLM performance in reasoning tasks.

You don't need Langchain; here's how to do Retrieval-Augmented Generation without it

Vigneshwarar’s Newsletter • 3 HN points • 18 Sep 23
🕹 Technology AI NLP Machine Learning Data processing Information Retrieval
  1. Retrieval-Augmented Generation (RAG) pipeline can be built without using trendy libraries like Langchain
  2. RAG technique involves retrieving related documents, combining them with language models, and generating accurate information
  3. RAG pipeline involves data preparation, chunking, vector store, retrieval/prompt preparation, and answer generation steps
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Will data served as language models overtake the role of semantic/linked data?

Living Systems • 2 HN points • 09 Aug 23
🕹 Technology Language Models Information Retrieval Artificial Intelligence
  1. The role of language models in serving data is being considered over semantic/linked data.
  2. Language models make data self-sufficient and self-describing, reducing the need for complex data schemas.
  3. Large language models present an opportunity for flexible data access and communication between models, potentially via linked data.

Trends & Open Questions in Retrieval for Foundation Models

Davis Treybig • 0 implied HN points • 01 Feb 24
🕹 Technology Information Retrieval Algorithms
  1. LLM applications resemble traditional recommendation systems, benefiting from information retrieval expertise.
  2. Building information retrieval pipelines for LLM products is complex and requires in-house development and tool curation.
  3. Trends include hybrid retrieval architectures, multi-stage rerankers, and evolving index management structures.

Zoo of RAGs

Shchegrikovich’s Newsletter • 0 implied HN points • 11 Feb 24
🕹 Technology AI Machine Learning Information Retrieval Natural Language Processing
  1. Retrieval Augmented Generation (RAG) improves LLM-based apps by providing accurate, up-to-date information through external documents and embeddings.
  2. RAPTOR enhances RAG by creating clusters from document chunks and generating text summaries, ultimately outperforming current methods.
  3. HiQA introduces a new RAG perspective with its Hierarchical Contextual Augmentation approach, utilizing Markdown formatting, metadata enrichment, and Multi-Route Retrieval for document grounding.