The challenge [with RAG] is that most corner-cutting solutions look like they’re working on small datasets while letting you pretend that things like search relevance don’t matter, while in reality relevance significantly impacts quality of responses when you move beyond prototyping (whether they’re literally search relevance or are better tuned SQL queries to retrieve more appropriate rows). This creates a false expectation of how the prototype will translate into a production capability, with all the predictable consequences: underestimating timelines, poor production behavior/performance, etc.
Recent articles
- The Summer of Johann: prompt injections as far as the eye can see - 15th August 2025
- Open weight LLMs exhibit inconsistent performance across providers - 15th August 2025
- LLM 0.27, the annotated release notes: GPT-5 and improved tool calling - 11th August 2025