However, the overall capability of the chatbot to fully meet user needs received a lower average score (3.1/5.0), highlighting the need for further improvements.
Live traffic evaluations are essential for monitoring system behavior, identifying potential issues like hallucinations in production, and understanding performance on diverse live queries.
Would be interesting to know how much money was flushed down the toilet with these experts.
The 3.1/5.0 score in the Frontiers paper is a user satisfaction rating on feature completeness. Researchers were asked how well the system met all of their needs, including features that simply didn't exist yet at that point. It's a product maturity signal, not an accuracy or reliability number. The paper is also about a year old and the system has moved on significantly since.
On hallucinations, I'd push back on the framing a bit. The fact that we monitor for hallucinations isn't an admission that the system is hallucinating undetected. It's the opposite. Every sentence in the response is linked back to the exact page and verbatim quote from the source document, so a researcher can verify any claim in one click. We also run faithfulness scoring on live traffic every single day using RAGAS, so if the system starts drifting we catch it fast, not at some quarterly review.
And for the regulatory document drafting use case, every output is explicitly reviewed and approved by a qualified scientist before it goes anywhere. The system drafts, the human decides. That's not incidental; it's a design constraint baked into the architecture.
No LLM eliminates hallucination entirely. That's just the reality of the technology right now. So the engineering question becomes: how do you make it as unlikely as possible, and when it does happen, how fast do you catch it? That's what the retrieval pipeline, the reflection agent, the citations, and the daily evals are all doing. It's not a perfect answer, but it's a serious one.
Thanks Claude!
From the paper, "we collected feedback from 15 to 20 frequent users". Is it 15 or 20?
I lot of interpretive claims, it reads more like a marketing case study.
Thank you for answering the commenter’s question, your answer was informative and helpful.
However it currently reads like you generated it. Which is a surprise, when you’re trying to foster trust in the quality of your process.
The alignment process goes very quickly once you have all the fish in exactly one barrel. I think pulling data dynamically from the source systems is where this turns into a game of whack-a-mole.
The problem with dynamic fetch is that you don't get any kind of persistent or compounding gains. There are queries that you simply cannot run because you'd chew through your GitHub, et. al., API quotas. It takes over 48h to fully hydrate the database for GitHub items on my current project. But, once that process is complete I can query across things like issue comments and do crosscutting joins with the state of other vendor systems in milliseconds.
I am finding the MSSQL dialect to be quite agreeable to the OAI models. With absolutely no prompting they will bootstrap off information schema and extended description properties every single time. If you design the schema for your audience, the amount of "Jesus prompting" you will require is much better controlled.
But that does make it more complex to build simple information retrieval use cases.
In my client's business, the idea of having all their customer knowledge contained in one global scope is a fantasy, not a fear.
I suppose if you were granting access to users outside the business that this could become a concern, but I haven't encountered anyone who is interested in that yet.
Mid-2026, we have very large context windows, and much smarter models than we did in 2024 when this was built. If I were to tackle this today I'd ask a current frontier model to work through the source data and design a hierarchy that would give it the ability to sift through the content itself by drilling down as it sees fit, and I expect it would nail that.
I'll take the opportunity to note that if you're running solid evals, you'll have data to back the efficacy of your system. If you are seeing a hallucination rate of 1%, then you certainly should be working on your harness/toolset/context/prompting etc.
Saying "1% hallucination rate..." is akin to saying "30,000mi lifespan for [modern japanese make engine]". Something is wrong.
Hmm...
The first sentence makes it seem like they just used to improve sentence structure etc but the second line makes it seem like they used it for 90% of the work. Which one is true?
I'd love to see the number of man hours that led to that sentence, and how proud they were to have come up with it.
Not sure how you manage to measure Faithfulness and Answer Relevancy on the live system, without the ground truth.
Good that you have evals in place, but the user satisfaction score might suggest running ablations on the system would be beneficial. I would start by reducing the iterations and unnecessary steps from the agent.
WE'RE WORKING ON AN ARCHITECTURE, YOU CAN OUR EARLY TESTERS IF YOU WANT
This is an ongoing working project since 2024, I would like to see some KPI metrics to back off any productivity /job satisfaction improvement in the research department, or what have you, at Bayer.
Monthly average token usage would be another interesting information to read about. Paired with any latency numbers (time to first token, for example).
I think right now I'm mostly disappointed with agents writing code as they always degrade the quality of the codebase after a while, and the same goes for writing in general which just requires a ton of editing and mostly just sounds good but doesn't have a lot of substance in the end. I think you can really tell that these systems are trained to just produce plausible streams of text, especially in longer artefacts you notice that locally the inner consistency of what they produce is great but globally it really falls apart, it's like seeing the limits of their "intelligence".
For search however I really like AI, it has improved information retrieval so much for me where before I had to think about which keywords to use and combine and which filters to apply, describing what I'm looking for in plain text and then having the AI find it for me feels magical. Recently I wanted to find an artist that I heard in some old episode of the KEXP runcast (a running podcast), and I didn't remember anything except that it was rap with a kind of monotone voice a fast beat and a strong accent. Googles' agent asked a few clarifying questions and after a few rounds it found the artist for me, Genesis Uwusu. That's why I think Google will win in the AI assisted search market, they just have the best integration between fast and reasonably "smart" agents and high quality search data. Claude or ChatGPT are too slow and don't have fast enough data retrieval it seems, using them for search feels quite sluggish in comparison.
My experience with using LLMs for search is that they do _not_ have laser precision. Far from it in fact. If you want to retrieve documents with laser precision AI is the wrong tool. If you want a fuzzy, lossy synthesised query response based on those documents, LLMs are great.
> teaches an O’Reilly course on building production-ready RAG applications
isn't this basically saying that you are a scammer? or am I paranoid?
The data you need to get into context for a small model, vs a big boy frontier model, vs a fine tuned open weight big boy- are all very different. I can understand what they're doing here, and most of the 'why', but- not all of the why.
The model gets it wrong on occasion and I check the input file with Claude/Opus and it just laughs at how simple it is to get the document right
And in the back of my mind I’m thinking why am I not just sending the file through Opus
But my off-the-cuff, uninformed opinion is that the precise structure doesn't matter too much, and the impact these structures really have is that they allow More Tokens for the Token Furnace.
There are some well documented advantages of decomposition...that's why the industry favours microservices over monoloths.
YAGNI almost always applies to microservices, and the coordination overhead and boilerplate they add introduces immense costs, especially for smaller companies.
This homogenisation of architecture around Netflix size engineering has really cost our industry a lot.