5-50 examples, I am skeptical that this can even produce good results. Hm, on their website they include a cost example with 100,000 tokens, so this must surely be more than 5-50 examples?
PROMPT ENGINEERING
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Adding Chat History to PromptTemplate with Llama2
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You can now add {chat_history} into PromptTemplate. Here's how you can use Replicate running Llama2 with a PromptTemplate:
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UT Austin NLP Lectures: From Basics to LLMs and Beyond
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Natural Language Processing – UT Austin A concise series of NLP lectures from UT Austin. Covers a vector of topics from basics of machine learning, NLP fundamentals, models(BERT, BART, T5, GPT-3…), and hot topics/trends in LLMs including instruction tuning, chain-of-thoughts,
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Evaluating RAG Systems: Insights from Application Developers
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It's always insightful to hear from actual application developers on how they approach evaluation That's why I'm excited to have @pedro_pxp on our webinar tomorrow that is focused on evaluating RAG systems! https://
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New LLM Survey Report on Production Adoption and Customization Techniques
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Check out our new #LLM survey report to get a lowdown on the current state of LLMs in #production. Topics covered: LLM adoption Top use cases Key challenges when productionizing LLMs Techniques for customization incl. #finetuning and more! https://
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Vector Store Document Loader Integration for Prompt Enhancement
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VectorDB Document Loader Many of you have asked: how to use retrieved documents from vector store into the prompt? With this new integration, you can now do so! Even with the flexibility of specifying a minimum score for documents to be included.
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ConversationalQAChain Templates LangChain Marketplace
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Chat history variable Use previous chat history inside the prompt. You can now create your own ConversationalQAChain from scratch using LLMChain + PromptTemplate, and more customization! A template is readily available in the marketplace.
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New Short Course on Fine-tuning Large Language Models
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New short course on Fine-tuning LLMs! Many developers are moving beyond only prompting, to also fine-tuning LLMs – that is, taking a pre-trained model and training it further on your own data, which can deliver superior results inexpensively. In this course, @realSharonZhou, CEO… pic.twitter.com/kAocaAgX0k
— Andrew Ng (@AndrewYNg) 23 août 2023New short course on Fine-tuning LLMs! Many developers are moving beyond only prompting, to also fine-tuning LLMs – that is, taking a pre-trained model and training it further on your own data, which can deliver superior results inexpensively. In this course, @realSharonZhou
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Open Sourcing Prompts: The New Code Sharing Model
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The idea of open sourcing prompts keeps coming up. @monkchips first clued me into the idea using Midjourney: you see and learn from others' prompts. If prompt engineering is a new way to code (as @mikeloukides has noted), then "open sourcing" prompts makes sense
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GPT-3.5 Turbo Fine-tuning Now Live for Early Customers
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GPT-3.5 Turbo fine-tuning is now live. Early customers have had great results, sometimes reaching GPT-4-level performance or beyond on their specific task. Really excited to see what people will build: