gk1

37 ms·

691.
Airbyte Connector for Pinecone(pinecone.io)
2 points by gk1 3y ago|0 comments
692.
Deploying Open-Source LLMs for Rag(pinecone.io)
1 points by gk1 3y ago|0 comments
693.
Fine-Tuning OpenAI's GPT 3.5 Turbo(pinecone.io)
1 points by gk1 3y ago|0 comments
694.
Poetry: Python Packaging and Dependency Management(github.com)
27 points by gk1 3y ago|15 comments
695.
Simple Systems Have Less Downtime (2020)(gkogan.co)
3 points by gk1 3y ago|0 comments
696.
Options for Solving Hallucinations in Generative AI(pinecone.io)
1 points by gk1 3y ago|0 comments
697.
The Easiest Part of LLM Applications Is the LLM(generatingconversation.substack.com)
1 points by gk1 3y ago|0 comments
698.
No New Categories (2020)(gkogan.co)
2 points by gk1 3y ago|0 comments
699.
LSH in NumPy C for some fun but no profit(softwaredoug.com)
1 points by gk1 3y ago|0 comments
700.
Deploying Open Source LLMs for Rag with SageMaker – Pinecone(pinecone.io)
1 points by gk1 3y ago|0 comments
701.
How to Use Jupyter Notebooks for Machine Learning and AI Tasks(pinecone.io)
1 points by gk1 3y ago|0 comments
702.
by gk1 3y ago
Full disclosure: I work for Pinecone, which is conspicuously absent from the write-up despite being the first and most popular vector database. While it’s great to see efforts to make sense of the (admittedly noisy) vector database market,
703.
The AI Progress Paradox(amistrongeryet.substack.com)
1 points by gk1 3y ago|0 comments
704.
by gk1 3y ago
Interesting, thanks for sharing. We also found[1] that context stuffing leads (generally) to worse quality results, as did a recent study.[2] But that's for question-answering use cases and not summarization. [1] https://www
705.
by gk1 3y ago
How would guardrails be applied through fine-tuning? I haven't heard of that, so genuinely curious. What I'm familiar with is guardrails applied on top of the LLM. For instance through prompting, managing the available data insi
706.
by gk1 3y ago
As other commenter said, fine-tuning is a costly and time-consuming affair. You can use RAG and have a separate namespace[1] for each of your customers in the vector database, so that it only searches through a specified customers' dat
707.
by gk1 3y ago
How much do you make use of Claude's large context window, and has it helped? Or did you basically revert back to small context size + multiple steps?
708.
You probably don’t need to fine-tune an LLM(tidepool.so)
181 points by gk1 3y ago|73 comments
709.
Can’t Hear the Dialogue in Your Streaming Show? You’re Not Alone(nytimes.com)
2 points by gk1 3y ago|3 comments
710.
AI-Powered and Built with... JavaScript?!(pinecone.io)
1 points by gk1 3y ago|0 comments
711.
AI-Powered and Built with… JavaScript?(pinecone.io)
6 points by gk1 3y ago|0 comments
712.
NeMo Guardrails: The Missing Manual(pinecone.io)
3 points by gk1 3y ago|1 comments
713.
The Art of Finding(deepset.ai)
23 points by gk1 3y ago|2 comments
714.
Can Pinecone and Other Vector Databases Prevent LLM Hallucinations?(revelry.co)
1 points by gk1 3y ago|0 comments
715.
The Economic Case for Generative AI and Foundation Models(a16z.com)
1 points by gk1 3y ago|0 comments
716.
Marketing lessons from Pinecone’s journey from seed to 10k signups/day(kylepoyar.substack.com)
1 points by gk1 3y ago|0 comments
717.
Chat with your data using OpenAI, Pinecone, Airbyte, LangChain(blog.langchain.dev)
5 points by gk1 3y ago|0 comments
718.
Retrieval Augmented Generation: An Overview(pinecone.io)
1 points by gk1 3y ago|0 comments
719.
by gk1 3y ago
Clickbait title. The article essentially says if you're just going for a short walk on a trail, you can get by with trail-runners. Which... duh. > "But he notes that trail-running shoes can capably handle a light or moderate hi
720.
by gk1 3y ago
Our blog post on the topic shows test results: https://www.pinecone.io/blog/hybrid-search/ TL;DR - doing hybrid vector + keyword search provides more relevant results for text searches than vector search alone. A

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