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Building LLM Products Panel // LLMs in Production Conference Part II

Aug 18, 2023
46:01
Snipd AI
Panelists George Mathew, Asmitha Rathis, Natalia Burina, and Sahar Mor discuss building products with LLMs, emphasizing transparency, control, and explainability. They explore the challenges of prompting in language models and provide tips for avoiding impersonation and hallucination. They highlight the importance of feedback loops in improving language models and discuss the economic components of using APIs and inference calls. The panel concludes with excitement about the conference and promotion of their own podcast.
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Podcast summary created with Snipd AI

Quick takeaways

  • Evaluating and mitigating risks associated with LLM hallucinations is crucial, prompting techniques like prompt chaining and explicit error specifications can help avoid incorrect outputs.
  • Continuous feedback loops with users and refining models over time is important for improving the fluency and accuracy of LLM outputs.

Deep dives

Understanding the Value and Challenges of LLMs in Production

LLMs have gained significant attention and are being used in various production applications. However, the panelists highlight the need for evaluating and mitigating risks associated with LLM hallucinations. They discuss the importance of fluency in LLMs and identify creative use cases where LLMs can excel, such as writing fiction or children's stories. Additionally, they emphasize the significance of accuracy in decision-making use cases and suggest using domain-specific specialized models. The panelists also delve into the concept of prompting and share insights on how to build effective prompts for LLMs. They address concerns about hallucination and provide techniques to avoid incorrect outputs, such as prompt chaining, semantic caching, and explicit error specifications. Furthermore, the discussion touches upon evaluating LLM performance and model selection, considering factors like cost, availability, performance benchmarks, and the evolving landscape of smaller and more powerful models.

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