A
Adam Bertram
Guest
Do not wait for a wrong answer to become a forwarded screenshot.
By the time a bad AI response lands in a customer email, board packet or internal decision thread, the question is no longer “why did retrieval miss?” It is “why did we have to learn this from the person least supposed to find it?”
Most teams treat a weak retrieval-augmented generation (RAG) answer as an output problem. They rerun the prompt, swap the model, polish the system instruction and hope the next answer behaves.
That is the wrong first move.
The answer is the last scene in the story, not the plot. The real story starts earlier, when the system decides where to look, which source fragments matter and whether the retrieved material can support the answer. If that trail is wrong, the final response can sound polished and still betray you.
Progress Agentic RAG gives that investigation a place to start because the answer is not a standalone artifact. It sits next to retrieved context and evaluation signal. You cannot fix a knowledge gap by staring at the sentence that came out; you have to inspect the evidence behind it.
The evaluation layer, REMi, makes the miss harder to wave away. It scores Context Relevance, Answer Relevance and Groundedness so you can read patterns instead of vibes:
Treating those signals as one blob called “AI quality” turns remediation into theater. The team debates prompts and models while the gap waits in the knowledge base.
The most useful signal in an agentic system is often the one nobody wants to read: the trail of failed or low-confidence interactions, from the question asked to the material retrieved, answer shown and feedback left behind.
In Progress Agentic RAG, Activity Logs give you that record. The log turns a bad answer into a sequence you can inspect instead of an anecdote people argue about.
A failed answer leaves a trail:
Read that sequence together and the article practically writes its own incident report. The customer team was not asking an exotic edge case. The policy existed, but the retriever surfaced the old version. The answer was grounded in source material, but the source material was obsolete. The user gave it a thumbs-down because the system answered the wrong version of the question.
That story has a fix. “The model was bad” does not.
The expensive mistake is rushing from “bad answer” to “rebuild the knowledge base.” That leap feels decisive. It also burns time in the least targeted way available.
Classify the gap first.
Empty retrieved context can indicate that the Knowledge Box is missing the information. Weak context needs a sequence: first check whether the Knowledge Box contains the information, then tune search or RAG strategy if it does. Grounded answers with low answer and context relevance point to wrong context being retrieved. Repeated thumbs-down feedback marks a user-facing failure that deserves priority even if the pipeline technically returned something.
Once you can name the failure, the next move gets smaller. Missing content needs a source owner. Hidden content needs an access review. Poorly surfaced content needs retrieval tuning. Outdated content needs governance with teeth, not another folder of PDFs nobody will maintain.
This is where the emotional stakes matter. A knowledge gap is not an abstract content hygiene issue. In a tense renewal, it is the answer your support team repeats with confidence. Inside the company, it can become the outdated policy an employee follows or the executive update that has to be corrected after the room moves on.
The smartest objection is fair: why not just keep adding documents until the gaps disappear? Because volume does not cure ambiguity. A larger pile of poorly structured knowledge gives the retriever more ways to be wrong. If the problem is structural, do not add more documents. Use enrichment techniques such as data augmentation agents or a dedicated Graph extraction agent so the relationships in your existing material are easier to retrieve. Otherwise you are feeding the system more content while leaving the blind spot intact.
One corrected answer is not the win. A repeatable review loop is.
Start with a small ritual your team can keep. Review recent questions without answers and questions with low context relevance, then use activity-log REMi query data to inspect the records behind them. Sort by customer impact and ownership. Pick the first few that matter. For each one, read the question, context, answer and feedback in one pass. Then decide whether the fix belongs to content, permissions, retrieval strategy or evaluation policy.
Do not call the issue closed when the answer sounds better. Rerun the original prompt and compare the new trail against the old one. Check whether the retrieved material changed for the reason you expected, whether the relevant score moved and, when citations are enabled, which source paragraphs the answer used. If not, you improved the performance, not the system.
That distinction is the whole argument. Knowledge-gap work is not content maintenance with AI branding. It is operational risk management for the answers your business is starting to trust.
So take a side. Stop treating weak answers as one-off annoyances. Treat them as the earliest warnings your knowledge base will ever give you. Pull a week of low-confidence and thumbs-down interactions. Then classify the first ten failures and assign owners before the same blind spot becomes someone else’s screenshot.
These are the practical questions that usually surface once teams start treating weak answers as diagnostic evidence instead of isolated misses.
The owner should be whoever can change the source of truth. Evaluation policy can sit with an AI platform owner, but remediation has to land with the person who controls the policy page, product note, support article or process document. If the reviewer can only file tickets, the loop has a handoff problem.
Treat the disagreement as a review trigger. Activity logs can expose both user feedback and REMi score fields, so inspect the prompt, retrieved context, answer and user signal together before changing content or retrieval.
Continue reading...
By the time a bad AI response lands in a customer email, board packet or internal decision thread, the question is no longer “why did retrieval miss?” It is “why did we have to learn this from the person least supposed to find it?”
Retrieval Failures Are A Warning, Not A Glitch
Most teams treat a weak retrieval-augmented generation (RAG) answer as an output problem. They rerun the prompt, swap the model, polish the system instruction and hope the next answer behaves.
That is the wrong first move.
The answer is the last scene in the story, not the plot. The real story starts earlier, when the system decides where to look, which source fragments matter and whether the retrieved material can support the answer. If that trail is wrong, the final response can sound polished and still betray you.
Progress Agentic RAG gives that investigation a place to start because the answer is not a standalone artifact. It sits next to retrieved context and evaluation signal. You cannot fix a knowledge gap by staring at the sentence that came out; you have to inspect the evidence behind it.
The evaluation layer, REMi, makes the miss harder to wave away. It scores Context Relevance, Answer Relevance and Groundedness so you can read patterns instead of vibes:
- Low context relevance can signal missing or poorly retrieved evidence.
- Low answer relevance shows the answer is not matching the question.
- Low groundedness warns that the response drifted away from retrieved source material.
- No useful context means the Knowledge Box check comes before model tuning.
Treating those signals as one blob called “AI quality” turns remediation into theater. The team debates prompts and models while the gap waits in the knowledge base.
Make the Log Tell the Story
The most useful signal in an agentic system is often the one nobody wants to read: the trail of failed or low-confidence interactions, from the question asked to the material retrieved, answer shown and feedback left behind.
In Progress Agentic RAG, Activity Logs give you that record. The log turns a bad answer into a sequence you can inspect instead of an anecdote people argue about.
A failed answer leaves a trail:
- First, the user’s question reveals real demand.
- The retrieved context exposes what the system believed mattered.
- Evaluation scores show whether that retrieval path held up.
- Feedback tells you whether the answer survived contact with the person using it.
Read that sequence together and the article practically writes its own incident report. The customer team was not asking an exotic edge case. The policy existed, but the retriever surfaced the old version. The answer was grounded in source material, but the source material was obsolete. The user gave it a thumbs-down because the system answered the wrong version of the question.
That story has a fix. “The model was bad” does not.
Classify the Gap Before You Spend the Quarter
The expensive mistake is rushing from “bad answer” to “rebuild the knowledge base.” That leap feels decisive. It also burns time in the least targeted way available.
Classify the gap first.
Empty retrieved context can indicate that the Knowledge Box is missing the information. Weak context needs a sequence: first check whether the Knowledge Box contains the information, then tune search or RAG strategy if it does. Grounded answers with low answer and context relevance point to wrong context being retrieved. Repeated thumbs-down feedback marks a user-facing failure that deserves priority even if the pipeline technically returned something.
Once you can name the failure, the next move gets smaller. Missing content needs a source owner. Hidden content needs an access review. Poorly surfaced content needs retrieval tuning. Outdated content needs governance with teeth, not another folder of PDFs nobody will maintain.
This is where the emotional stakes matter. A knowledge gap is not an abstract content hygiene issue. In a tense renewal, it is the answer your support team repeats with confidence. Inside the company, it can become the outdated policy an employee follows or the executive update that has to be corrected after the room moves on.
The smartest objection is fair: why not just keep adding documents until the gaps disappear? Because volume does not cure ambiguity. A larger pile of poorly structured knowledge gives the retriever more ways to be wrong. If the problem is structural, do not add more documents. Use enrichment techniques such as data augmentation agents or a dedicated Graph extraction agent so the relationships in your existing material are easier to retrieve. Otherwise you are feeding the system more content while leaving the blind spot intact.
Turn the Blind Spot Into a Cadence
One corrected answer is not the win. A repeatable review loop is.
Start with a small ritual your team can keep. Review recent questions without answers and questions with low context relevance, then use activity-log REMi query data to inspect the records behind them. Sort by customer impact and ownership. Pick the first few that matter. For each one, read the question, context, answer and feedback in one pass. Then decide whether the fix belongs to content, permissions, retrieval strategy or evaluation policy.
Do not call the issue closed when the answer sounds better. Rerun the original prompt and compare the new trail against the old one. Check whether the retrieved material changed for the reason you expected, whether the relevant score moved and, when citations are enabled, which source paragraphs the answer used. If not, you improved the performance, not the system.
That distinction is the whole argument. Knowledge-gap work is not content maintenance with AI branding. It is operational risk management for the answers your business is starting to trust.
So take a side. Stop treating weak answers as one-off annoyances. Treat them as the earliest warnings your knowledge base will ever give you. Pull a week of low-confidence and thumbs-down interactions. Then classify the first ten failures and assign owners before the same blind spot becomes someone else’s screenshot.
FAQ
These are the practical questions that usually surface once teams start treating weak answers as diagnostic evidence instead of isolated misses.
Who Should Own the Knowledge-Gap Review Loop?
The owner should be whoever can change the source of truth. Evaluation policy can sit with an AI platform owner, but remediation has to land with the person who controls the policy page, product note, support article or process document. If the reviewer can only file tickets, the loop has a handoff problem.
What If User Feedback Disagrees With The REMi Score?
Treat the disagreement as a review trigger. Activity logs can expose both user feedback and REMi score fields, so inspect the prompt, retrieved context, answer and user signal together before changing content or retrieval.
When Should You Change Content Instead of Retrieval Strategy?
Change content when source material is missing or stale, or when the logs show that the source itself is unclear. Change retrieval strategy when the right material exists but the system keeps surfacing the wrong evidence or only works with unusually narrow prompts. The point is to diagnose the failure class first so a small content fix does not become a platform project.Continue reading...