question answering
Beyond Market Intelligence keeps question answering in one place: 5 stories so far. The section currently leads with “Measure Embedding Relevance: A New Approach to Retrieval Benchmarking”, “Choose the right tool first, not just the AI default”, and “Build a Knowledge Layer Where Every Query Traverses a Living Graph”. Retrieval benchmarks often feel like they reward models for gaming the test rather than finding the right answer. Retrieval answers one kind of question, but it is not the whole toolkit. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every question answering story on Beyond Market Intelligence, newest first.
Measure Embedding Relevance: A New Approach to Retrieval Benchmarking
Retrieval benchmarks often feel like they reward models for gaming the test rather than finding the right answer. The team at Qonto noticed this gap and built a metric that ties relevance more directly to real product questions. They paired it with a dedicated dataset, and the results point to a more honest measure of embedding quality. It is a practical step toward benchmarking that actually reflects how teams retrieve information.
Choose the right tool first, not just the AI default
Retrieval answers one kind of question, but it is not the whole toolkit. Classifying a request, matching free text to a reference list, or cleaning OCR noise each has a cheaper, more direct method. The real engineering is knowing which tool to reach for. That practical focus makes this a useful read for anyone feeling stuck in RAG's limitations. For a deeper look at how LLMs handle structure, explore our related piece on paragraph and token space.
Build a Knowledge Layer Where Every Query Traverses a Living Graph
Most retrieval systems treat query wording as the failure point, but this approach argues otherwise: retrieval quality should be a property of the system, not the question's phrasing. By rebuilding the knowledge layer with graph traversal on every query, bitemporal edges, and two-threshold entity resolution, the architecture shifts the burden from user precision to systemic design. It's a pragmatic, future-focused move. For a deeper look at how structure shapes AI navigation, our related piece on paragraph structure in LLMs pairs well here.
When Every Passage Holds the Answer, RAG Needs a New Shape
Most RAG pipelines retrieve a single top passage and call it a day. But some questions don't work that way. Listing questions demand every relevant passage, not just the highest-scoring one. This pipeline walks through that silent failure point and the pipeline shape that actually handles it. It's a practical look at a real limitation, and the reasoning is clear. For readers who want to go deeper, "Exploring Paragraph Structure: How LLMs Navigate Token Space" offers a useful follow-up on how token coordinates shape retrieval.
Turn One Messy Question Into Four Clear Signals for Smarter RAG
A single messy question string hides a lot of structure. This piece breaks down how question parsing turns that raw input into four typed fields, each one feeding a distinct downstream call for retrieval and generation. It is a practical, grounded look at making RAG systems less brittle and more intentional. If you are still wrestling with vague prompts, this is worth your time. For more on how LLMs navigate structure, explore our related piece on paragraph structure.