How Retrieval and fine tuning as complementary

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Retrieval and fine tuning are complementary in the context of AI systems. Retrieval allows for the implantation and deletion of information within the system, enabling it to behave in desired ways and learn new skills. Fine tuning, on the other hand, determines how tasks are accomplished by guiding attention and synthesizing conclusions based on information. While fine tuning does not change facts, retrieval provides the actual data for operation. In the context of retrieval augmented papers, fine tuning allows the model to better utilize data obtained from retrieval, highlighting their complementary nature.

Anton Troynikov, cofounder of Chroma, joins Nathan Labenz to discuss the importance of keeping the retrieval-augmented generation (RAG) loop in house, what it means for Chroma to be in “wartime” mode right now, and how much of the data going into Chroma has never been in a database before. If you need an ERP platform, check out our sponsor NetSuite: http://netsuite.com/cognitive.

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LINKS:

Part 1 with Anton: https://youtu.be/ogy37CdIljg

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@labenz (Nathan)

@atroyn (Anton)

@eriktorenberg (Erik)

@CogRev_Podcast

TIMESTAMPS:

(00:00:00) - Introduction by Nathan, setting up the conversation with Anton

(00:02:16) - Anton articulates Chroma's mission to build a horizontally scalable system

(00:03:06) - Rise in popularity of retrieval-augmented generation (RAG)

(00:06:03) - Chroma's focus on delivering a horizontally scalable cloud service for vector search and storage

(00:08:07) - Nathan describes his experience building a RAG application for a client profiling use case

(00:10:27) - Anton advises measuring retrieval quality and maximizing relevant information returned

(00:15:05) - Sponsors: Netsuite | Omneky

(00:17:02) - Popular use of open source vs. proprietary embedding models like Anthropic's Ada

(00:19:30) - The importance of keeping the RAG loop in house and not relying solely on external APIs

(00:27:41) - The huge amount of unstructured data that can now be processed by AI

(00:30:40) - Providing a unified interface to structured and unstructured data

(00:33:15) - Much of the data going into Chroma has never been in a database before

(00:38:47) - Categories of organizations adapting to AI: legacy, AI-native, and AI-first

(00:40:55) - Where Chroma is seeing most of its growth right now

(00:46:20) - Interpretability work like Anthropic's circuit evaluation

(00:52:23) - Anton believes new tooling can make latent spaces accessible without AI expertise

(01:06:08) - Scaling constraints between search indexes vs. application databases

(01:09:10) - Potential for time as a dimension in embedding spaces

(01:13:46) - Likelihood of missing results due to representational issues vs. approximate nearest neighbor

(01:15:22) - Automatically handling small data sets without needing elaborate indexing

(01:17:20) - Anton's perspective on whether OpenAI will build its own database

(01:19:43) - Partnering with OpenAI and other labs to increase use of their models

(01:21:19) - Anton's experiments probing GPT's reasoning abilities with Game of Life

(01:25:41) - Closing thoughts on the conversation

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