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LLM Lessons learned (2024)

https://www.youtube.com/live/c0gcsprsFig
https://applied-llms.org/

Key points

  1. Foundation Models: One of the participants mentions training a foundation model from scratch using $50 million in DC money ( likely referring to deep learning computers). This is presented as a key step towards achieving success.
  2. Iterating to Success: The group discusses the importance of iterating on ideas, similar to Charles’ “zero to one” approach. They compare this process to traditional experimentation with new products.
  3. Offline Experimentation: The conversation turns to offline experimentation, where evals (evaluation metrics) are used to quickly cycle through different versions of a product.
  4. Zero-to-One Improvements: Participants discuss focusing on small, incremental improvements that add value to the user experience.
  5. Collaborative Effort: The group expresses appreciation for their collaboration and the resulting media report, which has had a significant impact on the community.

More details

1. Foundation Models and Iterative Development

  • Importance of Iteration: Developing AI products requires a systematic, iterative approach similar to software engineering practices. Evaluation (evals) must be integrated throughout the development cycle rather than being an end-stage task.
  • Data-Centric Focus: Effective development relies heavily on managing data quality and understanding idiosyncrasies in datasets. Data literacy and evaluation processes must be emphasized at all stages.

2. Evaluation (Evals) in AI Development

  • Domain-Specific Evals: Generic evaluation tools are insufficient for building robust AI systems. Instead, custom evaluations tailored to specific use cases are necessary to ensure meaningful insights.
  • Teaching Evaluation Approaches: Tools like “Scratch for evals” simplify understanding and implementing evals, enabling non-experts to measure progress effectively. This approach is crucial for building confidence among developers and fostering process literacy.

3. Building AI Systems: A Systems-Level Approach

  • System Durability: Instead of over-focusing on specific models (e.g., GPT-3, GPT-4), attention should shift to creating robust pipelines for evaluations, retrieval systems, and fine-tuning. These components offer long-term value regardless of model updates.
  • Textbook ML Concepts in Practice: Borrowing from established machine learning design patterns, like opposability in RAG (retrieval-augmented generation) or systematic evaluation, is critical for sustainable development.

4. Addressing the Talent and Knowledge Gap

  • Misconceptions about AI Roles: Overemphasis on tool mastery (chains, agents) for “AI engineers” neglects critical skills like data literacy and evaluation. This creates stagnation post-MVP and leads to unrealistic expectations.
  • Effective Hiring Practices: Integrating data cleaning and understanding tasks into hiring evaluations can identify candidates with practical, applicable skills.

5. Collaboration Across Disciplines

  • Stakeholder Engagement: Trust-building with users and stakeholders is achieved through transparency, early involvement of domain experts (e.g., UX designers, healthcare professionals), and continuous user feedback.
  • Prototyping and Deployment: Rapid prototyping with feedback loops ensures better alignment with user expectations, while gradual rollouts mitigate risks.

6. Evaluations as Core to Development

  • Evals for Progress Measurement: Regular assessments during development prevent guesswork and provide concrete metrics for improvement.
  • Avoiding Evaluation Overload: Using too many generic metrics without contextual relevance can lead to misdirected efforts. Focused, goal-driven evaluations yield better outcomes.

7. Democratizing AI Development

  • Lowering Barriers to Entry: Simplified tools and frameworks for evaluation and data analysis make AI development accessible to smaller teams and startups without extensive resources.
  • Data Inspection Is Non-Negotiable: Despite automation capabilities, manual data inspection remains critical to identify anomalies, understand performance, and debug effectively.

Overall Takeaways

The conversation emphasizes the importance of foundational practices—data management, domain-specific evaluations, and iterative system design—in building reliable AI applications. It also critiques over-reliance on flashy demos and underscores the value of collaboration, stakeholder trust, and realistic skill expectations in ensuring long-term success.

#llm

Page last modified: 2024-12-09 23:29:05