Generating Conversation: RLHF and LLM Evaluations with Nathan Lambert (Episode 6) https://
bit.ly/3Rlp1Hd
#AI #MachineLearning #DeepLearning #LLMs #DataScience
PROMPT ENGINEERING
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RLHF and LLM Evaluations with Nathan Lambert
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The Rise and Potential of LLM Based Agents
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2/ The Rise and Potential of LLM Based Agents – a comprehensive overview of LLM based agents; covers from how to construct these agents to how to harness them for good.
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Fine-tuned ChatGPT integration within Flowise platform
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Fine-tuned ChatGPT Use your fine-tuned OpenAI model within Flowise!
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Maximize ChatGPT Experience Using Custom Instructions Guide
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You can 10x your experience with ChatGPT by using Custom Instructions. I wrote a guide on how to do it:
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Chunking Techniques for Better AI Context Organization
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it definitely does this and as @bwatson suggests, chunking tends to help for this kind of use case it's also not as important to me that it gets every last detail—more just giving me a zoomed out view that helps me organize myself
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Handling Large Documents Exceeding Context Windows
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ah. curious to see approaches to large documents that wont fit in the context window 🙂
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How Does AI Understand Document Context for Code Generation?
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you said you were going to get it to write code to modify the document. when you give it instructions "change last ? to ." the function call is "replace_text("…?", "….")" (or something. it knows what the last sentence is…. how?
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JavaScript Retrieval Functionality in LangChain Framework
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Great to have this retrieval functionality in JS See the underlying prompt here: https://
smith.langchain.com/hub/jacob/mult
i-query-retriever
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Interactive Virtual Panel on LLMs: Open-source vs Commercial Solutions
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Don't miss our interactive virtual panel with #LLM experts from @llama_index
, Bank of America & @predibase Topics: Open-source vs. commercial LLMs High-value use cases Tips for customization (#RAG, #finetuning) Overcoming common pitfalls https://
pbase.ai/3PDq4Qz -
LLM Output Validation: Ensuring Robust and Predictable Applications
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An output validation step ensures that an LLM application is robust and predictable. In this article, we look at LLM output validation and how to implement it using @guardrails_ai
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