I’ve been saying this for a while now. Over the next couple of years, *most* fine-tuning work will become many-shot prompting.
PROMPT ENGINEERING
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Building LLM Agents with Tool Use and Data Analysis
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Building LLM Agents with Tool Use This great video from our friend @JayAlammar at Cohere walks through how to build a data-analyst LLM agent with Cohere's newest Command R+ model Agents are complex, but Jay does a great job of breaking them down! https://
youtube.com/watch?v=5drn2D
O7gNY
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Training LLMs to Retrieve Information When Uncertain
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4). When to Retrieve? – presents an approach to train LLMs to effectively utilize information retrieval; it first proposes a training approach to teach an LLM to generate a special token, , when it's not confident about the answer to a question.
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Augment Your LLM Using RAGs NVIDIA Course
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4. Augment your LLM Using RAGs https://
learn.nvidia.com/courses/course
-detail?course_id=course-v1:NVIDIA+S-FX-16+v1
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Build Email Q&A App Using Embedchain and Streamlit
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5. Ask question about your emails and get the answer • Create a text input for the user to enter their question using 'st.text_input()'
• If a question is asked, get the answer from the Embedchain app and display it using 'st.write()' -
Prompt Engineering with LangChain: Free LinkedIn Learning Course
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Prompt Engineering with LangChain This LinkedIn course by @DataScienceHarp is a great resource for getting started It's over 5 hours long, and Harpreet has been a fantastic member of the community since the early days. Big thanks for all the effort! https://
linkedin.com/learning/promp
t-engineering-with-langchain/create-powerful-llm-driven-applications
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Multi-Agent RAG Online Workshop: Exploring Agentic Workflows
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Multi-Agent RAG Online Workshop If there’s anything better than agentic RAG, it’s multi-agent RAG! In this event, we’ll explore the big idea behind “multi-agent” applications. These types of workflows combine multiple independent agents, which can be structured to work
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Cohere Build Day Toronto: Knowledge Agents with Command R
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Cohere Build Day is in Toronto! Follow us as we build knowledge agents with Command R and R+. This is your chance to share your projects, ask our team questions, and leave feedback.
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Fine-tuning Models with New Data: Demonstrating Practical Capabilities
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I'm ready to believe fine-tunes can do useful things with new facts from their fine-tuning data… but I need to first interact with a working demo to get there!
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Gorilla: Fine-tuning LLM capabilities for improved function calling
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This Gorilla? That looks like fine-tuning for improved capabilities like function calling, not for baking in extra knowledge https://
gorilla.cs.berkeley.edu