Vinay Kumar Sankarapu’s Post

Our recently-published guide on LLM prompting explains how models like DBRX and Llama 3 process and respond to inputs. It focuses on the mechanics of prompt handling rather than just listing best practices, aiming to give developers a deeper understanding of LLM behavior. Here are some of the key insights for AI engineers and developers: - Prompt Composition: What you type isn't always what the model sees. User inputs are often inserted into templates that include additional context, chat history, and hidden instructions. - Token-by-Token Generation: LLMs generate responses one token at a time, with each new token influenced by the entire prompt and all previously generated tokens. This impacts how we structure our prompts for optimal results. - Order Matters: The sequence in which information is presented in a prompt can affect the output. For instance, asking for an explanation before an answer yields different results than the reverse order. - Model-Specific Behavior: Different models may respond differently to the same prompt, necessitating prompt adjustments when switching between models like DBRX and Llama 3. - System Messages: Often hidden from end-users, system messages set context, framing, and constraints for the model's responses. The guide emphasizes that while there are best practices, there's no universal "best prompt" for each task type. Experimentation and understanding of model behavior are key to crafting effective prompts for specific use cases. For hands-on practice with these concepts, we recommend using the Databricks AI Playground to compare different prompts, models, and generation parameters. See the first comment below for the full article 👇 #AI #LLM #PromptEngineering

#Hybrid_AI #improves_explainability by combining interpretable models with more complex ones, using layered decision-making processes, and providing complementary explanations from various perspectives.

#Hybrid_AI #improves_explainability by combining interpretable models with more complex ones, using layered decision-making processes, and providing complementary explanations from various perspectives.

#Hybrid_AI #improves_explainability by combining interpretable models with more complex ones, using layered decision-making processes, and providing complementary explanations from various perspectives.

Informativt om utveckling av open weights LLMs. Open-weights #GenAI models give companies greater control and transparency, but they need refinement to shine. Learn more: https://lnkd.in/dfVtGnmn

Hot Hot Hot 🔥 🔥 🔥 𝐃𝐨 𝐘𝐨𝐮 𝐖𝐚𝐧𝐭 𝐭𝐨 𝐔𝐬𝐞 𝐀𝐫𝐭𝐢𝐟𝐢𝐜𝐢𝐚𝐥 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 (𝐀𝐈) 𝐭𝐨 𝐏𝐞𝐫𝐟𝐨𝐫𝐦 𝐒𝐏𝐒𝐒 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐃𝐢𝐫𝐞𝐜𝐭𝐥𝐲? In this video, five AI APIs from different ones are reviewed, and one (𝕐𝕖𝕤𝕔𝕙𝕒𝕥 𝔸𝕀) that can perform statistical analysis, is suggested. Just write a prompt. 𝐓𝐨 𝐯𝐢𝐞𝐰 𝐭𝐡𝐞 𝐞𝐧𝐭𝐢𝐫𝐞 𝐭𝐮𝐭𝐨𝐫𝐢𝐚𝐥, 𝐜𝐥𝐢𝐜𝐤 𝐘𝐨𝐮𝐓𝐮𝐛𝐞 𝐥𝐢𝐧𝐤: https://lnkd.in/dSKXMeRJ https://lnkd.in/dSKXMeRJ https://lnkd.in/dSKXMeRJ Write Your Idea 👋👋👋

In our latest blog, Plotly CPO Chris Parmer and Engineering Manager Greg Wilson explore how AI is speeding up manual benchmarking for data tools. ⚡ From comparing the performance of #Pandas, #Polars, #DuckDB, and #SQLite to accelerating the process of testing and validating data solutions, #AI helps cut through the complexity and speed up results. Discover how we're using AI to generate datasets, run benchmarks, and compare code syntax across libraries — helping us zero in on what works best and saving valuable time. Read more 👉 https://lnkd.in/esdmkVJ8

Have you heard about system cards for AI models? I recently learned about this while exploring OpenAI's new "o1" model. As I researched Chain-of-Thought techniques, the basis for o1’s advanced reasoning capabilities, I stumbled upon the concept of system cards. System cards are a bit like nutrition labels for AI models, offering crucial insights into a model's architecture, performance, and potential issues. They’re not as brief and short as a nutrition label, but I was surprised to note they were presented in easier to understand layman’s terms. I certainly learned A LOT about how the o1 model works. In general, system cards cover various aspects, from intended use cases to fairness considerations, propensity for deception and environmental impact. The entries on a system card are filled by internal and external auditors based on a thorough examination of the overall system and its components. They provide a holistic view of how complex AI systems operate, going beyond individual model documentation to show how multiple components work together. Biggest takeaway for me: As quick as we are to try out the next new, shiny AI tools, we ought to be responsible digital citizens. Before diving into the latest models, I urge you to review their system cards. They're not perfect, but they're invaluable for making informed decisions about an AI model’s use, and understanding their limitations. Have you checked out any system cards yet? What stood out for you? I've taken a closer look at OpenAI's o1 system card and shared some key takeaways in the comments. Check it out and let me know your thoughts! #AI #ResponsibleAI #EthicalAI #ArtificialIntelligence

Daniel Liden from the Databricks #DevRel team (and my awesome teammate) has created an informative and helpful guide on how to more effectively work with #LLMs - starting with the #prompt ! “There are already plenty of very good LLM prompting guides available online. Why are we writing another one? We think that, in addition to cataloging useful techniques, there is room for a prompting guide that connects specific techniques to a broader understanding of the structure and behavior of LLMs. You should come away from this series with a strong intuition of how models work and how to write prompts to solve your specific problems, not with a memorized list of prompt types.” https://lnkd.in/gr6GPiSs cc: Databricks Mosaic Research , #dbrx, #llama3

One of the most common questions we get at Serif Health is: "What is Serif doing with AI?" The truth is AI (at least as an acronym...) runs through everything we do: - First, we pioneered Automated Ingestion (AI 1.0) of 350 billion rows+ of payer machine readable files monthly -Then, we built an industry-leading A(P)I (AI 2.0) to traverse the data in real-time to power web apps, find care tools, and cost estimation -But now, we've used the latest in LLMs to add an actual AI assistant (AI 3.0) to our Signal platform! As we approach the end of the year, Serif Health is kicking off the holiday season with a free gift to all of our Signal platform subscribers: your own personal AI assistant to create custom visualizations, grab 2nd-order insights, and clarify any questions. Enjoy! Read more about it here: https://lnkd.in/gBbRxJFr