Daniel sent us a really layered one this time. He's been building out his consulting site at carrotcakeai.com — which, full disclosure, is his actual site, and he's very open about wanting clients — but the prompt is deeper than self-promotion. He's been working on a visual framework for explaining agentic AI, and in the process he realized something about what he calls latent value spaces. Specifically, he's looking at two under-exploited layers in AI systems: the prompt extraction layer and the output extraction layer. His core question is: if you took all the prompts he's ever sent us — which are publicly available on Hugging Face, by the way — how would you build a context extraction pipeline that mines those for persistent facts about him? And then, once you've got that memory layer running, how do you handle the self-healing problem? Facts that change over time, preferences that shift, contradictions that inevitably creep in as the database scales. That's the challenge.
Oh, this is a juicy one. And he's surfacing something that the industry has been dancing around but not really nailing. Everyone's obsessed with model capabilities — bigger context windows, faster inference, better reasoning — but the boring plumbing of what you do with the data that flows through these systems? That's where the actual compound value lives.
Before we dive into the plumbing, quick note — DeepSeek V four Pro is generating our script today.
Okay, so let's get into this. Daniel's essentially describing a two-stage pipeline. Stage one is extraction: take a corpus of raw prompts, which are messy, conversational, full of asides and context-switching, and pull out only the bits that constitute durable personal context. Stage two is maintenance: keep that context store consistent as new information arrives that might contradict or refine old information. The extraction part is genuinely non-trivial, but it's solvable. The maintenance part is where things get philosophically interesting.
Walk me through the extraction side first. If I handed you a JSON file with all of Daniel's prompts — and I've seen these, they're long, they ramble, they jump between personal anecdotes and technical questions — what's your first pass look like?
The first thing I'd do is resist the urge to over-engineer. You don't need a massive model for extraction. What you need is a structured prompt that acts as a classifier plus extractor. I'd batch-process the prompts through something lightweight — maybe a fine-tuned Phi four or a Mistral seven B variant running locally. The system prompt would be something like: "You are a context extraction engine. Given a user prompt, identify any statements that reveal persistent facts about the user. Classify each fact by type — demographic, preference, professional, relational, opinion — and assign a confidence score. Return only facts in structured JSON. Ignore transient statements, questions, and technical content.
You're separating the wheat from the chaff before it ever hits a vector database.
And that's the key insight Daniel's getting at with the latent value space idea. The prompts themselves contain two kinds of information. There's the explicit request — "explain how agentic AI works" — and then there's the ambient context that leaks through: "I live in Jerusalem," "I'm building a consulting practice," "I love pizza." Most systems treat the whole prompt as one blob and embed it. That's wasteful. You're polluting your context store with noise.
Daniel's point is that this extraction is almost trivially easy to implement if you're already using APIs. You just log the prompts, run them through a batch inference pipeline, and suddenly you've got a structured profile that enriches every future interaction.
And here's where I'd add a layer he hinted at but didn't fully spell out. There's a distinction between explicit context and inferable context. Explicit is "I live in Jerusalem." Inferable is trickier — if someone mentions they're navigating Israeli bureaucracy, you can infer they're probably in Israel, probably dealing with government systems. You can extract that with a second pass: after you've got your explicit facts, you run an inference layer that says, "Based on these prompts, what can you reasonably conclude about this person?" That layer needs a higher confidence threshold, and you flag those inferences as derived rather than stated.
This is starting to sound like a multi-pass architecture. Pass one extracts surface facts. Pass two infers implicit context. What's pass three?
Pass three is contradiction detection — but that bleeds into the self-healing side. Let me stay on extraction for one more minute, because there's a practical implementation detail that I think most people get wrong. Daniel mentioned PromptFu and versioning tools, and he's right that people don't think enough about prompt logging. But the real gold isn't in the prompt text alone — it's in the prompt paired with the system response and any user feedback. If you log the full triad — prompt, response, and whether the user accepted, edited, or rejected the response — you can start weighting context facts by how they correlate with successful interactions.
A fact that appears in prompts that led to high-satisfaction responses gets a higher persistence weight.
And that's a signal that becomes incredibly valuable when you get to the self-healing layer. But let me pause there — what's your read on the extraction architecture?
I think you're right about the multi-pass approach, but I'd push back on one thing. You said resist over-engineering, and I agree, but I think there's a case for using a more capable model for the inference pass. The explicit extraction pass — "I live in Jerusalem," "I have a son named Ezra" — that's straightforward entity extraction. Any decent small model can do it. But the inferential pass, where you're trying to figure out that someone is probably a parent based on scattered references to bedtime routines and school runs — that requires actual reasoning. I'd run that through a larger model, maybe Claude or Gemini, with a system prompt that's carefully tuned to avoid hallucinated inferences.
And I'd add a rule: any inferred fact gets a mandatory "needs verification" flag. The system shouldn't treat inferences as ground truth until the user confirms them, or until they're corroborated by multiple independent prompts.
Which brings us to the second half of Daniel's challenge — the self-healing problem. He gave the pizza example: in January he loves pizza, in April he's off pizza and into Greek food. A naive vector database just accumulates both facts, and now your context store has a contradiction. How do you resolve that?
This is where things get hard, and I'll be honest — I don't think anyone has a production-grade solution yet. But there are approaches that get you most of the way there. The simplest is temporal weighting: every fact gets a timestamp, and when you retrieve context, you bias toward recency. If Daniel said he loves pizza three months ago but said he's into Greek food last week, the Greek food preference gets higher weight in retrieval. That handles a lot of cases.
It doesn't handle the case where an old fact is still true and a new fact is actually the error. "I was born in Ireland" is old but durable. "I'm thinking of moving to Tel Aviv" is recent but might be a passing thought, not a settled fact.
So temporal weighting alone isn't enough. You need a fact type taxonomy that includes a stability score. Demographic facts — birthplace, family members' names — these are high stability. Preferences — food, music, hobbies — these are medium stability, they change on the scale of months or years. Emotional states and transient opinions — low stability, they might change day to day. When you detect a contradiction, you don't just compare timestamps. You compare stability scores. A low-stability recent fact should override a low-stability old fact. But a low-stability recent fact should never override a high-stability old fact without explicit user confirmation.
The system needs a conflict resolution policy that's more nuanced than "most recent wins.
Much more nuanced. And this is where Daniel's batch reprocessing idea becomes crucial. He mentioned running an iterative context updater every thousand prompts. I love that cadence. Every thousand prompts, you take your entire accumulated fact database and run a reconciliation pass. For each entity — Daniel's food preferences, Daniel's location, Daniel's professional focus — you look at all the facts tagged to that entity, cluster them by similarity, and flag clusters that are internally contradictory. Then you generate a reconciliation prompt: "The system has recorded that the user loves pizza and also that the user is off pizza and prefers Greek food. Which is currently accurate?" You surface that to the user, or you use an LLM to guess the resolution based on recency and surrounding context.
The user-facing approach is interesting. It turns the context store into something the user can curate. But Daniel's challenge is specifically about doing this automatically — a self-healing store, not a user-managed profile.
Right, and for full automation, I think the state of the art is what some teams are calling "fact lifecycle management." Each fact in the database isn't just a static entry — it's an object with a state machine. A fact can be proposed, confirmed, contested, deprecated, or superseded. When a new fact arrives that contradicts an existing confirmed fact, the existing fact moves to contested status. The system then looks for corroborating evidence across the entire prompt history. If the new fact is supported by multiple recent prompts, it gets confirmed and the old fact gets deprecated. If the new fact appears only once and the old fact has multiple corroborations, the new fact gets flagged as low-confidence and the old fact stays confirmed.
This is starting to sound like a miniature legal system for personal data.
It kind of is. And you need evidentiary standards. How many independent prompts need to support a fact before it's considered confirmed? How recent does the corroboration need to be? What's the threshold for reopening a deprecated fact if new evidence supports it? These are design decisions that depend on the use case. For a movie recommendation agent, you might want high sensitivity to preference changes — if someone says they're tired of action movies, you pivot immediately. For a professional context system, you might want higher confirmation thresholds before changing something like someone's stated expertise.
Daniel also raised the point about different cadences of change. How you feel today versus where you work versus where you were born. Three completely different time scales.
This maps cleanly onto the stability score concept. I'd propose a four-tier system. Tier one: immutable facts — birthplace, date of birth, family relationships that haven't changed. These never get auto-deprecated. Tier two: slowly changing facts — career role, city of residence, long-term projects. These might change every few years, and they need high corroboration to update. Tier three: preference facts — tastes, interests, tools of choice. These change on the order of months, and you want moderate sensitivity to new signals. Tier four: state facts — current mood, what someone's working on today, whether they're tired or energized. These are highly transient and should decay quickly, maybe even with an explicit time-to-live.
A fact like "Daniel is building carrotcakeai.com" would be tier two or three — it's a professional focus that might shift over months or years. "Daniel lives in Jerusalem" is tier two. "Daniel loves pizza" is tier three, and if he says he's off pizza, the system should be fairly quick to update that.
And the tier system also determines retrieval behavior. When you're assembling context for a new prompt, you pull tier one and two facts aggressively — they're always relevant. Tier three facts you pull selectively, based on semantic similarity to the current query. Tier four facts you pull only if the current query seems to involve emotional state or immediate circumstances.
Let's talk about the actual implementation for a minute. Daniel's offering his prompt dataset on Hugging Face as a testbed. If someone wanted to build this today, what's the stack look like?
I'd start with a PostgreSQL database with pgvector extension for the vector storage. Structured facts go in relational tables with the state machine logic I described. The embeddings go in a vector table for semantic retrieval. The extraction pipeline runs as a batch job — could be triggered by a cron job, could be event-driven when new prompts arrive. For the extraction model, I'd use something cheap and fast for pass one — Phi four, as I mentioned, or even a distilled BERT variant if you want to go really lightweight. For pass two, the inference layer, I'd use Claude or GPT-4o, something with strong reasoning. The reconciliation pass runs on whatever cadence makes sense — Daniel's suggestion of every thousand prompts is reasonable for a personal context system.
The whole thing could be orchestrated as an agentic workflow itself. Which is meta, but fitting.
An agent that manages another agent's memory. But here's the thing — Daniel's instinct about batch inference is spot-on. You don't need to do this in real time. Context extraction is an offline task. Prompts accumulate, the batch job runs, the database updates. Real-time context injection is a separate thing — that's what happens when a new prompt comes in and the system retrieves the current context to prepend to the prompt. But the extraction and reconciliation can happen asynchronously.
One thing I want to flag — Daniel mentioned that his prompts and our scripts are open source, and he's essentially inviting people to use this dataset to test context extraction. There's something interesting there about the nature of the data. Podcast prompts are a weird genre. They're performative, they're edited, they're designed to be heard by an audience. The context you extract from them might be different from what you'd extract from private chat logs.
Public prompts have a different signal profile. Someone might present themselves differently in a podcast prompt than in a private conversation with an AI assistant. The extraction system needs to account for the context in which the prompt was written. A fact stated in a public forum might be more curated, more deliberate. A fact extracted from a private chat might be more candid. Neither is necessarily more true — they're just different lenses.
That's actually a feature, not a bug, if you're building a system that's meant to understand someone across multiple contexts. You want to know both how they present publicly and what they say privately.
The full picture emerges from the triangulation. Which brings me to something Daniel didn't explicitly ask but that I think is the logical next step. Once you've built this context extraction layer and it's humming along, maintaining a rich, self-healing profile — what do you do with it? The obvious answer is "inject it into prompts to personalize responses." But there's a subtler use case: context-aware routing.
In an agentic system, you've got multiple tools, multiple models, multiple possible workflows. When a prompt comes in, you need to decide how to handle it. A rich context profile lets you route more intelligently. If the system knows Daniel is a technical user who builds AI pipelines, it routes his queries to the advanced model with the full tool suite. If it knows he's asking about something in his wheelhouse, it might surface his own previous outputs as reference material. The context isn't just for personalization — it's for optimization of the entire agentic workflow.
That connects back to his jigsaw visual, actually. He said agentic AI is system prompts plus MCP plus tooling plus grounding, and the value is in how the pieces fit together. The context layer is the connective tissue. It's what makes the other pieces work together intelligently rather than just being bolted on.
And the latent value space concept he's describing — drawing arrows between components that aren't obviously connected — that's where the compound value lives. The arrow from prompts to context, from outputs to business wiki. These aren't the headline features. Nobody's raising venture capital for "better context extraction from prompt logs." But over time, those latent connections are what make a system smarter than its components.
Let's get concrete about the self-healing mechanism for a second. You mentioned a state machine for facts. What does that actually look like in code?
I'd model each fact as a row with columns for the fact ID, the user ID, the fact type, the fact value, the stability tier, the current state, the timestamp of last state change, a list of source prompt IDs that support this fact, and a list of source prompt IDs that contradict it. When a new extraction run produces a fact, the system checks for existing facts of the same type and value. If it finds a match, it just adds the new prompt ID to the supporting sources list and updates the timestamp. If it finds a fact of the same type but different value — a contradiction — it creates a new proposed fact in contested state, links it to the existing confirmed fact, and flags both for reconciliation.
The reconciliation logic?
The reconciliation logic runs periodically. For each contested pair, it evaluates: what's the stability tier of the fact type? What's the recency distribution of supporting prompts? How many independent prompts support each version? Is there a clear temporal pattern — like, was the old fact consistently supported for months and then a sharp switch, suggesting a genuine change, versus scattered contradictions that might be errors or outliers? Based on these signals, it either confirms the new fact and deprecates the old one, or keeps the old one and marks the new one as an anomaly, or surfaces the ambiguity for human review.
The human review escape hatch is important. There's a category of contradictions that no automated system should resolve unilaterally. If the system detects that someone has changed their stated profession, or their family structure, that's a signal that shouldn't be auto-resolved.
High-stability tier facts — tier one and two — should always require explicit confirmation before a state change. The system can propose the change, but it shouldn't execute it. Tier three preferences can auto-update with sufficient corroboration. Tier four state facts should auto-update aggressively.
Daniel also asked about the scaling problem. As the vector database grows, internal inconsistencies become more likely. How do you handle that at scale?
This is where you need what I'd call a "fact compaction" process. Over time, you don't need to keep every single extraction of "Daniel lives in Jerusalem" from every single prompt. You can compact those into a single confirmed fact with metadata about when it was first observed, when it was last confirmed, and how many times it's been corroborated. The raw extractions can be archived. The active database only needs the current state plus enough provenance to do reconciliation. This dramatically reduces the surface area for inconsistencies.
The compaction process itself can be an agentic workflow. Another batch job that runs less frequently — maybe monthly — and consolidates redundant facts, resolves clear-cut contradictions, and flags the hard cases.
And here's a thought I've been turning over. Daniel mentioned agentic interviews as a proof of concept — having an AI agent proactively ask you questions to build a profile. He said it works but he doesn't think it's the default way. I actually think it might be underrated. Not as a replacement for passive extraction, but as a complement. The passive extraction layer builds a profile from ambient data. But it has blind spots — things you've never mentioned in prompts because they never came up. An active interview layer could periodically surface questions: "I've noticed you've never mentioned dietary restrictions. Do you have any?" or "I've inferred you work in AI, but what specific areas?" This fills gaps in the profile and also serves as a verification mechanism for inferred facts.
It addresses something that's been nagging at me about the whole extraction approach. It's inherently backward-looking. You're building a profile from historical data. But people change, and sometimes the change happens without leaving a clear trail in the prompts. Someone stops eating pizza, but they never explicitly say "I no longer eat pizza" — they just stop mentioning it. The passive extraction system might never catch that. An active interview system could ask.
The ideal system combines passive extraction for ongoing signal, active interviews for gap-filling and verification, and a reconciliation engine for consistency. Three legs of the stool.
Let me pull on a thread Daniel mentioned in passing. He said he built a prototype that separates prompts from context — taking an incoming prompt, splitting out the contextual information, and feeding them differently into the generation pipeline. That's an interesting architectural choice. Instead of just prepending context to the prompt, you're actually restructuring how the model receives information.
Yes, and this is an under-explored area. Most systems just concatenate everything — system prompt, retrieved context, user prompt — into one big text blob and send it to the model. But there's evidence that models process different kinds of information differently. Contextual facts might be better placed in the system prompt, where they're treated as background assumptions. The user's actual query might be better placed in the user role, where it's treated as the task. Separating them could improve both coherence and relevance.
Daniel's prototype does this by using a system prompt that explicitly says "here is the context," followed by the extracted facts, and then "here is the user's question," followed by the query stripped of contextual asides. The model doesn't have to disentangle what's context and what's question — it's already structured.
That's smart. And it connects to a broader point about prompt engineering that I think gets lost in the agentic AI hype. Everyone's excited about tool use and multi-step reasoning, but the humble system prompt is still doing enormous amounts of work. A well-structured system prompt that cleanly separates context from instruction from examples — that's not glamorous, but it's probably worth more in terms of output quality than a lot of the more sophisticated agentic scaffolding.
Which brings us back to Daniel's jigsaw. The system prompt is one of the pieces, and it needs to fit precisely with the context layer, the tool definitions, the grounding sources. If any of those pieces is misaligned, the whole thing degrades.
The alignment is dynamic. As the context layer updates, as new tools are added, as the model itself gets upgraded, the system prompt might need to change. This is why I think the "prompt extraction layer" Daniel's describing is actually a two-way street. You extract context from prompts, but you also need to extract insights about how prompts are performing and feed those back into prompt design. The promptFu-style versioning he mentioned is half the picture. The other half is analytics: which prompts are producing high-quality outputs, which are producing hallucinations, which are producing user corrections? That feedback loop closes the circle.
The full architecture is: prompts flow in, extraction layer pulls context, context enriches future prompts, performance data flows back to prompt design, and the whole thing iterates. The latent value spaces Daniel drew as arrows on his diagram are feedback loops.
And that's the genuine insight here. Agentic AI isn't a static system you set up once. It's a set of feedback loops that compound over time. The organizations that win with AI won't be the ones with the best models — they'll be the ones with the best data flywheels. The context extraction layer, the output-to-wiki pipeline, the prompt analytics loop — these are the flywheels.
Let's talk about the output extraction layer for a moment, because Daniel mentioned it but focused more on the prompt side. He said: if you have useful chats, put them in your wiki to cut down on repetitive prompting. That sounds simple but I think there's depth there.
There's enormous depth. Most organizations are sitting on thousands of AI conversations that contain solved problems, clever approaches, edge cases handled well. That knowledge evaporates the moment the chat window closes. An output extraction layer would identify high-value exchanges — maybe flagged by user feedback, maybe identified by an LLM evaluating the conversation — and distill them into structured knowledge base entries. Over time, your wiki becomes a curated collection of battle-tested solutions, and new prompts can retrieve from it before reinventing the wheel.
This is where Daniel's "latent value space two" — the arrow from outputs to business wiki — becomes a real competitive advantage. It's not just about efficiency. It's about institutional memory. People leave organizations. Chat logs get lost. But if you're systematically mining outputs and feeding them into a structured knowledge base, you're building an asset that compounds.
The compound effect accelerates. The more entries in the wiki, the more likely a new query finds a relevant precedent, which means faster and better responses, which means higher-quality outputs to feed back into the wiki. It's a virtuous cycle.
I want to circle back to something Daniel said at the very end of his prompt. He mentioned that some facts don't change — where you're born — and others are in a state of rapid flux. He said any true context mining system has to account for both the extraction classification and the self-healing aspect. I think he's understating the difficulty of the classification problem itself.
Classifying a fact as immutable versus transient isn't always obvious from the fact alone. "I live in Jerusalem" — is that a settled fact or a temporary one? For Daniel, it's been true for years. For a student on a semester abroad, it's transient. The system can't know the difference without building a model of the person over time.
The stability tier isn't inherent to the fact type — it's partly inherent and partly learned from the individual's history. Someone who's moved cities three times in two years has a different location stability profile than someone who's lived in the same place for a decade. The system should start with reasonable defaults — location is medium stability, birthplace is high stability — and then adjust based on observed patterns for that specific user.
The tier system itself should be adaptive.
And that's a whole additional layer of complexity. But it's the right kind of complexity, because it makes the system personalized. Not just in what it knows about you, but in how it knows it — how it weighs evidence, how it handles contradiction, how quickly it adapts to change. Two users with the same facts but different change patterns should have different context management policies.
This is starting to feel like we're building a theory of mind for an AI system. Which I suppose is exactly what we're doing.
And it's worth stepping back and appreciating how far the field has come. A few years ago, the conversation was "how do we stop models from hallucinating?" Now we're talking about maintaining coherent, self-healing personal knowledge graphs that adapt to changing facts over time. The ambition has shifted from "make the model not lie" to "make the system know the user.
The knowledge graph framing is interesting. I've been thinking of this as a vector database problem, but it's really a knowledge graph problem with a vector retrieval layer on top. The facts have structured relationships — Daniel lives in Jerusalem, Jerusalem is in Israel, Daniel works in AI, his website is carrotcakeai.A graph structure captures those relationships naturally. The vector embeddings handle semantic similarity for retrieval. The state machine handles temporal consistency.
The graph structure makes contradiction detection easier. If the system has a node for Daniel's location with an edge pointing to Jerusalem, and a new extraction suggests Tel Aviv, that's a direct contradiction on the same edge. In a pure vector database, those two facts might not even be recognized as contradictory — they're just two vectors that happen to be about location.
The architecture is: extraction layer produces structured facts, facts are stored in a knowledge graph with state machine logic, graph is indexed with vector embeddings for semantic retrieval, reconciliation engine runs periodically to detect and resolve contradictions, and the whole thing feeds into a context injection layer that structures prompts intelligently. Did I miss anything?
The feedback loop from output quality back to prompt design. But that's a separate subsystem. For the core context pipeline, I think you've got it. And the beautiful thing is, none of this requires frontier model capabilities. The heavy lifting is in the architecture, the state machine logic, the reconciliation policies. The models are just components.
Which is Daniel's whole point with the jigsaw. The model is one piece. The system prompt is one piece. MCP is one piece. The magic is in how they connect.
The latent value spaces — the arrows between pieces — are where the real work happens. Anyone can buy access to a good model. Not everyone can build the data flywheels that make the system smarter over time.
Let me pose a challenge. Daniel's dataset is public. Anyone listening could download it and build this. What's the minimum viable version that would actually demonstrate the concept?
Minimum viable would be: take the prompts, run them through a single-pass extraction with a system prompt tuned for personal facts, store the results in a simple database with timestamps, and build a retrieval endpoint that prepends the most recent and most frequently corroborated facts to new prompts. No state machine, no reconciliation, no knowledge graph. Just extraction plus retrieval. That gets you maybe sixty percent of the value with twenty percent of the complexity.
The next increment?
Add contradiction detection. When the extraction pipeline finds a fact that conflicts with an existing fact, flag it. Don't auto-resolve — just surface it. That immediately makes the system more trustworthy, because you're not silently accumulating inconsistent context.
Then the reconciliation engine.
Then the reconciliation engine, which is the hardest piece. And honestly, for most use cases, I think automated reconciliation is optional. Human-in-the-loop reconciliation — where the system periodically surfaces proposed changes and the user confirms or rejects them — is simpler and more reliable. The self-healing ambition is admirable, but it's also where the most subtle failures happen.
That's a pragmatic note to end the technical discussion on. Self-healing is the aspiration, but human-curated context with good tooling might be the practical sweet spot for most organizations.
The system should do the heavy lifting of extraction, classification, and contradiction detection. But for high-stakes changes, the human should have the final say. That's not a limitation — it's good design.
And now: Hilbert's daily fun fact.
Hilbert: The national animal of Scotland is the unicorn.
...right.
Here's the forward-looking thought I want to leave listeners with. Daniel's prompt is ostensibly about a technical architecture — how to build a context extraction and self-healing pipeline. But underneath that, he's asking a bigger question: what does it mean for an AI system to know you over time? Not just remember your name and preferences, but understand the shape of your life — what's fixed, what's fluid, what's changing. That's not a solved problem. It's barely a well-defined problem. But the pieces are there, and the dataset to experiment with is public. If you're interested in this stuff, go build something with it. The prompts are on Hugging Face.
If you want to hire the person who thinks about these problems at this level of depth, carrotcakeai.He mentioned it, I'm mentioning it — the man knows what he's doing. Thanks to Hilbert Flumingtop for producing. This has been My Weird Prompts. Find us at myweirdprompts.
We'll be back soon.
