For a given compute budget, how would you reason about investing in a larger, more powerful LLM versus investing in a more sophisticated retrieval system? What evaluation results would inform this decision?

When deciding between investing in a larger language model (LLM) or a more sophisticated retrieval system for a fixed compute budget, the key factors are the specific use case, the quality of existing data, and the trade-offs between generalization and precision. A larger LLM excels at tasks requiring broad reasoning, creativity, or handling ambiguous inputs, while a retrieval system shines in scenarios where accuracy depends on accessing specific, structured, or up-to-date external data. For example, a customer support chatbot might benefit more from retrieval if it needs to pull exact product details, while a creative writing tool would prioritize a powerful LLM. The decision hinges on whether the problem demands deeper understanding or faster access to precise information.

To evaluate this, start by testing baseline performance. Measure the LLM’s accuracy on tasks without retrieval, then compare it to a smaller LLM paired with a retrieval system. For instance, if a medical QA system using a 13B-parameter LLM achieves 70% accuracy alone but jumps to 85% when augmented with a retrieval system accessing clinical guidelines, retrieval is likely worth prioritizing. Key metrics include precision (how often retrieved data is relevant), recall (how much relevant data is found), and latency. If retrieval consistently reduces hallucination or improves factual correctness—like reducing errors in legal document analysis—it may justify the investment. Conversely, if tasks require nuanced reasoning (e.g., summarizing technical research), a larger LLM might outperform retrieval-augmented smaller models.

Finally, consider scalability and maintenance. A retrieval system requires ongoing updates to its data corpus and indexing, while a larger LLM demands more upfront training and inference costs. Run cost-benefit analyses: If scaling the LLM from 7B to 70B parameters only improves results marginally (e.g., +5% on a benchmark) but triples inference costs, reallocating budget to retrieval could be better. Similarly, if latency is critical (e.g., real-time translation), a retrieval-heavy approach might introduce delays. Prototype both approaches with tools like FAISS for retrieval and open-source LLMs (e.g., Llama 3) to compare real-world performance. If retrieval cuts compute costs by 40% while maintaining quality, it’s a clear win; otherwise, prioritize model size.