Four Building Blocks for Effective LLM Systems: Evaluations, Retrieval, Generation, & Feedback Collection

Unlock the power of LLMs: Evaluations, Retrieval, Generation, Guardrails, and Feedback - A Guide to Building Better AI Systems

1. Eugene Yen will discuss four patterns for building language model (LM) systems and products, based on his popular article.
2. The four patterns are evaluations (evals), retrieval, augmented generation, guard rails, and collecting feedback.
3. Evals help understand the effectiveness of prompt engineering, retriever augmentation, or fine-tuning in LM systems.
4. Eval-driven development uses evals to guide system building, serving as test cases and ensuring safety before deployment.
5. Building evals is challenging due to the lack of consistent approaches for LLMs, unlike conventional machine learning metrics.
6. Academic benchmarks like MMLU assess LMs on knowledge and reasonability using a prompt and multiple-choice question format, but there's no standard way to run them.
7. Simple formatting changes can significantly affect accuracy in these academic benchmarks, making model comparison difficult.
8. Outgrown academic benchmarks may not be suitable for specific tasks, as automated summarization evaluation scores might already surpass human ones.
9. When using LLMs, it's crucial to ensure that the chosen eval method is a good fit for the task at hand.
10. To build effective evals:
a. Start with a specific task and start small (e.g., an eval set of 40 questions).
b. Simplify tasks as much as possible, focusing on clear metrics like precision or recall.
c. If the task is open-ended, use strong LMs to evaluate output but be aware that this can be expensive.
11. Retriever-augmented generation allows adding knowledge to models using input context rather than relying solely on the model's knowledge.
12. Retrieving the right documents for LLMs is challenging due to the vast amount of information available.
13. LMs can't always see all retrieved documents, and their ability to use these documents effectively varies based on their position in the retrieval set.
14. Even with perfect retrieval, there might still be some mistakes due to factors like context complexity.
15. LMs struggle to determine if a retrieved context is irrelevant, leading to potential issues with factual consistency (hallucinations).
16. To address hallucinations in LLM outputs:
a. Use natural language inference tasks to classify the relationship between premises and hypotheses.
b. Apply these tasks at the sentence level for better accuracy.
c. Utilize sampling methods to compare summaries generated from an input document, checking if they are similar or grounded on the context document.
17. Collecting feedback is crucial in production systems to understand user preferences and improve models over time.
18. Explicit feedback can be sparse, while implicit feedback may have noise due to users' organic interactions with the product.
19. Examples of successful feedback collection include GitHub Copilot and MidJourney, which utilize implicit feedback through user actions.
20. Automated evals, starting with annotating 30-100 examples and then automating the process, are essential for faster iteration on prompt engineering, retrieval augmentation, fine-tuning, and safer
21. Reusing existing systems like BM25, metadata fetching, and recommendation system techniques can help build effective LM systems without reinventing the wheel.
22. User experience (UX) plays a significant role in LM products as it makes LLMs more accessible and user-friendly for collecting valuable feedback data.

Source: AI Engineer via YouTube

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