When deciding between investing in a larger LLM or a more sophisticated retrieval system for a fixed compute budget, the key is to analyze the specific problem you’re solving and the bottlenecks in your current system. A larger model might improve reasoning or generalization for tasks requiring deep contextual understanding, but it comes with higher training and inference costs. In contrast, a retrieval system can reduce the LLM’s workload by providing relevant context, making it more efficient for tasks where domain knowledge or real-time data access is critical. For example, if your application involves answering questions over a large corpus (e.g., customer support), retrieval can narrow the input space, allowing a smaller model to perform accurately. However, if the task demands synthesizing novel ideas or handling ambiguous inputs (e.g., creative writing), a larger model may be necessary.
Evaluation results should focus on task performance, latency, and cost trade-offs. First, measure baseline metrics like accuracy, F1 score, or task-specific KPIs for your current setup. Then, test how these metrics change when scaling the model (e.g., from 7B to 13B parameters) versus enhancing retrieval (e.g., adding dense passage retrieval or reranking). Track inference latency and compute costs for both approaches: a larger model may slow down responses and increase cloud expenses, while a retrieval system might add latency from database lookups but reduce the LLM’s processing time. For example, if adding retrieval improves accuracy by 15% with minimal latency increase, but scaling the model only improves accuracy by 5% while doubling costs, retrieval is likely the better investment. Additionally, evaluate how retrieval quality (e.g., recall@k) impacts downstream task performance—poor retrieval may nullify gains from a larger model.
Consider scenarios where one approach is clearly better. If your task relies heavily on external, structured, or frequently updated data (e.g., medical diagnosis using latest research), a retrieval system is essential. Conversely, if the task involves open-ended reasoning with no clear data dependencies (e.g., code generation), scaling the model might yield better results. Hybrid approaches can also work: use retrieval to handle factual queries and a smaller model for complex reasoning, optimizing compute allocation. For example, GPT-4 with retrieval outperforms larger models without retrieval in knowledge-heavy tasks, as shown in benchmarks like Natural Questions. However, if evaluations reveal that errors stem from the model’s lack of fundamental reasoning (e.g., math problems), scaling the model becomes necessary. Always prototype both options with your data to quantify trade-offs before committing resources.