Here's something I've been thinking about. We've generated nearly four thousand episodes of this podcast. And if I want to find every episode we've done about vector databases, or prompt engineering, or the economics of open source AI — good luck. The search is broken. Not broken in the sense that it doesn't return results. Broken in the sense that it returns results I know are incomplete, and I can't trust them.
This is exactly what Daniel sent us. He's been thinking about the same thing — that this project was never really meant to be consumed as a chronological feed. It was always supposed to be an audio library. Something you browse by topic, by concept, by question. But the cataloging infrastructure for that just hasn't caught up to the sheer volume of content. Four thousand episodes is a threshold. Linear consumption stops working. Discoverability becomes the bottleneck to the whole project's value.
It's not just our problem. Think about any long-running podcast. If you discover it today and it has four hundred episodes in the back catalog, you can probably skim titles and pick out the ones that interest you. At four thousand, that completely breaks down. You can't skim four thousand titles. You need a map.
And Daniel's question is essentially — how do you build a scalable, AI-driven cataloging system for something this size without manual effort, without drowning in duplicate tags, and without hitting the token limits that make the obvious approaches fall apart? He's tried some things. He's seen the failure modes. And he wants to know if there are agentic patterns or frameworks that can actually solve this.
Which is a genuinely interesting technical problem. Because the failure pattern he's describing aren't bugs — they're structural. You let an LLM generate tags freely, you get duplicates. You try to constrain it by passing the entire existing taxonomy, you blow up the context window. Neither approach scales.
What makes this trickier than it first appears is that the problem compounds with every new episode. It's not just that you have four thousand episodes to tag. It's that every time you add one, you have to decide whether its tags are new or just variations on tags you already have. The system has to learn without forgetting.
Let's unpack why a four-thousand-episode library breaks traditional search, and what we can learn from library science and AI agents to actually fix it.
The real problem here isn't actually search — it's organization. Search is downstream. If your tags are a mess, your search is a mess, no matter how good Algolia's ranking algorithms are. And Daniel's instinct about the two failure pattern is dead on. Let me make this concrete. Imagine episode one forty-seven, the one about the privacy gap. An LLM might tag that as "encryption," "E two E E," "metadata," and "privacy." Another episode on the same topic gets tagged "end-to-end encryption," "data privacy," and "surveillance." Those are semantically identical clusters, but string matching sees zero overlap. So a listener searching for "encryption" gets half the library and doesn't even know what they're missing.
Which is almost worse than no tags at all. At least with no tags you know you're flying blind. With bad tags you think you've found everything. You stop searching. You assume the library just doesn't have much on that topic.
That's the trust problem. Once a user realizes the tags are unreliable, they stop using them entirely. They go back to skimming titles or just giving up. All that cataloging effort becomes worse than useless — it actively erodes confidence in the system.
And that's failure pattern one — tag explosion from unconstrained generation. failure pattern two is the obvious fix that doesn't work. You think, fine, I'll just pass the entire existing taxonomy as a constraint — here are all the approved tags, stick to these. But do the math. Four thousand episodes, say three tags each on average. That's twelve thousand tokens just for the tag list before you've even included the episode title, description, or transcript you want the model to actually tag.
Even if you're using a model with a hundred twenty-eight thousand token context window, you're not out of the woods. The context window might technically hold it, but the model's attention gets diluted across all that constraint text. It starts forgetting tags it already used. Or worse, it starts hallucinating tags that aren't in the approved list because it lost track of what was in the middle of the prompt.
Positional bias is the term. The model pays most attention to the beginning and end of the context. Tags in the middle effectively don't exist. So it creates "natural language processing" even though "NLP" was sitting right there at position four thousand in the prompt. You haven't solved the problem — you've just made it more expensive and slower to fail.
It's worth pausing on why this is such a sticky failure pattern. The intuition is so reasonable — just give the model the list, tell it to stay within bounds. It feels like it should work. And when you test it on ten episodes, it does work. The model sees your twenty approved tags, picks the right ones, everything looks clean. You ship it. Then at episode three hundred, with nine hundred approved tags in the prompt, it starts silently drifting. You don't notice until a listener emails you asking why searching for "transformers" returns three different tag variations.
That's the insidious thing about scale-driven failures. They don't show up in the prototype. They only emerge once you've already committed to the approach and run it across the full corpus.
The question Daniel's really asking isn't "how do we tag episodes." It's "how do we build a system that knows what it already knows, without having to hold all of that knowledge in its head at once.
That's a question that shows up everywhere in AI engineering right now. It's the same problem you hit with retrieval-augmented generation, with long-running agent loops, with any system that has to maintain state across many operations. How do you give the model access to what it needs without overwhelming it?
That's where the agentic pattern comes in. The insight is to separate generation from normalization into two distinct stages, each with its own context. Stage one — you feed a single episode transcript to an LLM and say, give me three to five topic tags. No constraints, no taxonomy. It just reads the episode and produces whatever tags feel right. You run this independently for every episode in the library. Each call is tiny — maybe two thousand tokens of transcript, fifty tokens of output. Cheap, fast, no context pollution.
Stage one is basically a map operation. Each episode gets its own little universe where it doesn't need to know about any other episode. It's like giving a hundred librarians each one book and asking them to write down what it's about, without letting them talk to each other. You're going to get a lot of different ways of describing the same thing, and that's expected.
And you end up with a giant bag of raw tags — probably fifteen thousand of them across four thousand episodes, full of duplicates and near-duplicates and spelling variations. That's fine. That's what stage two is for. Stage two is a normalizer agent. Its job is to look at each candidate tag and decide — does this already exist in our canonical taxonomy, just spelled differently or phrased slightly differently? And it makes that decision using embedding similarity, not string matching.
You store the canonical tags in a vector database. Each tag gets embedded — OpenAI's text embedding three small, fifteen thirty-six dimensions. When a new candidate tag comes in, you embed it, compute cosine similarity against all existing canonical tags, and if it's above some threshold — say zero point eight five — you merge it. If nothing matches, you add it as new.
The math on this is surprisingly reasonable. Embedding four thousand episode descriptions at maybe two hundred tokens each costs about sixteen dollars total with that model. The vector database itself — something like Chroma or Pinecone's free tier — can handle a few thousand tag embeddings without breaking a sweat. Cosine similarity lookup across fifteen hundred canonical tags takes milliseconds. This isn't a research lab budget. This is a weekend project.
Which is what makes it viable for an open source, non-profit project. Daniel's not going to hire a team of taxonomy librarians. And honestly, even if he could, that approach has its own problems. Human taggers drift over time. The person tagging episodes in January has a slightly different mental model than the person tagging in June. You just trade algorithmic inconsistency for human inconsistency.
And the normalizer agent doesn't need the entire episode transcript in its context. It just needs the candidate tag, a short definition or context snippet, and the top five most similar existing tags from the vector store. That's maybe three hundred tokens per normalization call. You could run this on a laptop.
The pipeline is map then reduce. Map each episode through the tag generator independently. Then reduce by clustering all the raw tag embeddings — DBSCAN is the algorithm people reach for here because you don't need to specify the number of clusters in advance. It finds natural groupings based on density. All the variants of "machine learning" end up in one cluster. You pick the most representative tag from each cluster as the canonical label, and that becomes your taxonomy.
DBSCAN is perfect for this because tag space is messy. Some topics have dozens of near-duplicate variants. Some have exactly one. You don't want to force everything into K clusters when you don't know what K is. DBSCAN says, I'll group things that are close together and leave isolated points alone. An isolated tag is probably unique — no merging needed.
Let me give a concrete example of why that matters. Imagine you've got a cluster of fifteen tags all orbiting around "reinforcement learning" — "RL," "reinforcement-learning," "reinforcement_learning," "RLHF," and so on. DBSCAN pulls those together beautifully. But then you've also got one episode tagged "battery chemistry." That tag sits alone in embedding space, far from everything else. K-means would try to shove it into some cluster it doesn't belong in. DBSCAN just leaves it alone. That's the correct behavior.
Which brings us back to Daniel's question about Algolia. Why not just use Algolia's built-in faceting? The answer is, faceting works great once you have clean, normalized tags. But Algolia doesn't generate those tags for you. It indexes what you give it. If you give it "machine learning," "ML," and "machine-learning" as three separate facet values, that's what your users see — three different filters that all mean the same thing. The faceting engine doesn't know they're semantically identical.
From the user's perspective, that's actively confusing. They see "machine learning" with forty-two episodes, "ML" with thirty-eight, and "machine-learning" with seven. Do they need to check all three? Are these different things? They have no way to know.
The pipeline fixes that upstream. You run the map-reduce backfill once, produce a clean taxonomy, push those normalized tags into Algolia as facet attributes. Now the faceting actually works. And the real win is Algolia's AI Synonyms feature — you can take those merge clusters from DBSCAN and feed them in as synonym sets. Someone searches "ML," Algolia automatically expands it to also match episodes tagged "machine learning." No manual synonym list maintenance.
The whole thing is a batch job. You're not tagging episodes in real time as they're generated. You run the backfill across the entire four-thousand-episode corpus offline — map each episode, embed the raw tags, cluster with DBSCAN, normalize to canonical labels, push to Algolia. It runs once, maybe takes an hour, costs under twenty dollars in API credits.
For new episodes going forward, you don't need to re-run the whole thing. You just run the two-stage pipeline on each new episode as it's generated — generate candidate tags, normalize against the existing vector store, add new tags if they emerge. The taxonomy grows organically.
Here's a question — what happens when a new topic emerges that's adjacent to an existing cluster but not quite inside it? Say you've had a cluster around "language models" for years, and then "agentic AI" starts showing up. The new tags are close to the language model cluster but distinct enough that they might form their own cluster over time. How does the system handle that transition?
That's where the periodic re-clustering matters. In the steady state, with just incremental additions, a new tag like "agentic AI" might get normalized into the nearest existing cluster — maybe "autonomous systems" or "LLM applications." It gets merged when maybe it shouldn't be. But then when you re-run DBSCAN across the full accumulated tag set, suddenly there are fifteen episodes all tagged with variations of "agentic AI," and the density is high enough to form a new cluster. The re-backfill crystallizes it.
The incremental pipeline makes reasonable guesses in the moment, and the periodic batch re-run corrects any drift. That's a nice pattern. It's forgiving.
Once you have that pipeline running, the knock-on effect on the user experience are where things get interesting. Because you're not building a Dewey Decimal system here. You're not sitting down and deciding in advance that the top-level categories are "Technology," "Science," "Culture," and then subdividing from there. That approach breaks the moment a new topic emerges that doesn't fit neatly into your hierarchy.
Which is basically the story of every taxonomy ever designed by a committee. Someone invents a new thing and suddenly you're arguing about whether "agentic AI" goes under "Artificial Intelligence" or "Autonomous Systems" or whether you need a whole new branch. The committee meets, debates for three weeks, and by the time they decide, the field has moved on and there's a new thing that doesn't fit.
And with a podcast that covers everything from battery chemistry to diplomatic protocol, the hierarchy would be a nightmare to maintain. What the pipeline produces instead is a folksonomy — tags that emerge organically from the content itself, clustered by actual usage rather than predetermined categories. "Prompt injection" exists as a tag because enough episodes discuss it, not because someone decided it deserved a slot.
There's something philosophically satisfying about that. The taxonomy isn't imposed from above. It grows from the content. It's a map drawn by actually walking the territory.
Which means the browse experience on the website can be dynamic. Instead of a fixed category tree, you get a tag cloud where the size of each tag reflects how many episodes it covers. Or a drill-down that's generated on the fly from the normalized tag set — click "vector databases" and you see related tags like "embeddings," "semantic search," "RAG," with the connection strengths based on co-occurrence across episodes.
This is where the Algolia integration gets powerful in practice. Once those normalized tags are pushed as facet attributes, a listener searching for "prompt injection" doesn't need to guess which variant we used. The normalizer already merged "prompt injection," "injection attacks," and "jailbreaking" under the canonical tag "prompt injection." Algolia's faceted search returns all twelve episodes, ranked by relevance, with the tag displayed consistently.
The AI Synonyms feature handles the other direction too. Someone types "jailbreaking" into the search bar — which isn't even the canonical tag — and Algolia expands it to match everything tagged "prompt injection" because the synonym set was built from the DBSCAN clusters. You get the same twelve episodes either way.
That's the kind of thing that makes a library actually usable. The listener doesn't need to learn our vocabulary. They just type what they're thinking about, and the system bridges the gap.
Let me make the agentic framework concrete, because I think this is what Daniel's really asking for. You can implement this with something like LangGraph, or honestly just a straightforward Python workflow. Agent one is the TagGenerator — its prompt is basically, you are an expert podcast cataloger, read this transcript, extract three to five topic tags that capture what the episode is substantively about. No constraints, no taxonomy. It runs on every episode and dumps raw tags into a staging table.
Agent two is the TagNormalizer. Its prompt includes the current canonical taxonomy — but only the tags that are semantically nearby, pulled from the vector store. So it sees maybe five existing tags rather than fifteen hundred. Its instruction is: compare this candidate tag to these existing tags. If it's a near-duplicate, return the canonical version. If it's new, approve it for addition. It writes back to a SQLite or Postgres table that serves as the single source of truth.
The whole thing runs as a script. Not a real-time service, not a microservice architecture with message queues. A Python script that iterates through four thousand episodes, calls the generator, collects the raw tags, clusters them, runs the normalizer, and outputs a clean taxonomy. You run it once as a backfill, then set up a cron job or a webhook to process new episodes on ingestion.
I want to underline that point about not building a microservice architecture. There's a tendency in AI engineering right now to reach for LangChain and message queues and distributed workers for everything. But for a four-thousand-episode batch job, that's all overhead. A single Python script with a progress bar gets the job done and you can actually reason about what happened.
The elegance of keeping it as a batch job is that you can re-run the clustering periodically. New clusters emerge. "Agentic AI" didn't exist as a coherent topic in twenty twenty-three — if you ran the backfill then, it wouldn't appear. But run it again now, and episodes that touched on early agent patterns get regrouped under a cluster that wasn't visible before.
You can tune the granularity by adjusting the DBSCAN epsilon parameter — how close two embeddings need to be to count as the same cluster. A tighter epsilon gives you more specific tags. A looser one groups broader themes. You can experiment without changing any code, just a config value.
Which means Daniel can actually build this. The pieces are all off-the-shelf. OpenAI embeddings, a vector store like Chroma, DBSCAN from scikit-learn, Algolia's API for pushing the results. The whole thing fits in a single repository, runs on commodity hardware, and costs less than a nice dinner in API credits.
What does this mean for you, whether you run a podcast, a blog, or a video library? I think there are two actionable insights here that apply to basically any content cataloging problem.
The first one is the architectural insight. Never let a single LLM call both generate and constrain tags in the same context window. That's the trap. You're asking one model to be creative and disciplined simultaneously, across a context that's either too small to know the full taxonomy or too large to pay attention to all of it. Separate the two jobs.
Generation happens in isolation — each piece of content gets its own clean context. Normalization happens with access to the taxonomy, but only the relevant slice of it. That separation is what makes the whole thing scale. And it's not just about token limits — it's about giving each agent a job it can actually succeed at.
This pattern shows up everywhere once you start looking for it. Content moderation pipelines that separate flagging from policy enforcement. Code review systems that separate finding issues from suggesting fixes. Any time you're tempted to write one big prompt that does everything, ask whether you'd get better results from two smaller prompts with clear handoffs.
The second insight is that embedding similarity is your deduplication engine, not string matching. OpenAI's text embedding three small model costs basically nothing and catches variations that regex never will. Set a cosine similarity threshold — zero point eight five is a reasonable starting point — and anything above it gets flagged for merge. But here's the part people skip: keep a human review step for edge cases. The threshold catches ninety-five percent of duplicates automatically. The remaining five percent are things like "reinforcement learning" versus "RLHF" — related but not identical — where you want a person to make the call.
That human review doesn't need to be a full-time job. It's a CSV file with fifty borderline cases that you spend twenty minutes on once after the backfill runs. You just adjudicate the close calls.
Those close calls are interesting. They're the cases where reasonable people could disagree about whether two concepts are the same thing. "Prompt injection" versus "jailbreaking" — are those synonyms or distinct techniques? The answer probably depends on your audience and how technical they are. No embedding model can make that call for you.
If you're running any kind of content library — blog, podcast, video channel — the blueprint is implementable this weekend. Two-stage pipeline. Backfill your existing corpus first with a batch script. Then set up a webhook or cron job to tag new content on ingestion. LangChain can orchestrate it if you want a framework, but honestly a couple hundred lines of Python with the OpenAI client and scikit-learn does the job for free.
The nice thing about keeping it simple is you actually understand what the system is doing. When a tag merge happens that surprises you, you can inspect the embeddings, check the similarity score, and adjust the threshold. It's not a black box. It's just math you can look at.
There's one question I keep coming back to though. Should the final taxonomy be hierarchical — AI, then NLP, then Transformers nested underneath — or should it stay flat? A hierarchy makes browsing feel more natural. You drill down. But it also forces classification decisions at every boundary, and those boundaries get weird fast. Is "RLHF" a child of "reinforcement learning" or "AI safety"? It's both, depending on the episode.
You build a tree in twenty twenty-six, and by twenty twenty-eight you're stuffing square topics into round categories because nobody wants to restructure the whole thing. A flat tag system with co-occurrence links is messier to look at but far more honest about what the content actually contains. I'd argue for flat tags with dynamic grouping on the front end — let the browse interface generate pseudo-hierarchies from tag co-occurrence rather than baking them in.
The nice thing about that approach is that the pseudo-hierarchy can be different for different users. A researcher browsing the library might want to see "RLHF" grouped under "AI safety." An engineer might want it under "reinforcement learning." With co-occurrence data, you can surface both relationships without committing to either.
Which loops back to the maintenance question. As this library grows past ten thousand episodes, the agentic pipeline itself needs periodic re-clustering. Topics emerge that didn't exist when you ran the first backfill. "Agentic AI" is the obvious example — it's a major cluster now, but episodes from twenty twenty-three that touched on early agent patterns are probably scattered across "LLMs" and "tool use" and "autonomous systems.
You'd schedule a re-backfill. Re-embed all episode descriptions, re-run DBSCAN on the full tag set, let new clusters crystallize. The pipeline is the same — the cost is just recomputing embeddings for the growing corpus. At ten thousand episodes, that's maybe forty dollars in API credits.
The real long-term challenge isn't technical. It's knowing when the taxonomy has drifted enough to justify the re-run. You don't want to do it too often and destabilize the browsing experience. Too rarely and the catalog feels stale.
Which is a judgment call, not an algorithm. And that's where Daniel's human judgment matters more than any agent. The system handles the grinding work. He decides when it's time to refresh.
Now: Hilbert's daily fun fact.
Hilbert: In the nineteen fifties, archaeologists in the Atacama Desert unearthed a preserved Tang dynasty bureaucratic manual — a complete guide to the imperial examination grading rubric — buried in a camel saddlebag, likely carried there by a Silk Road official who died en route and whose body was never found.
...right.
You know, there's actually a connection here. That Tang dynasty manual was essentially a taxonomy for evaluating human knowledge. A thousand years later, we're still arguing about how to categorize what people talk about. The tools change, but the fundamental problem is the same.
That's either profound or a stretch, and I'm not sure which.
The question that stays with me is whether a flat folksonomy actually serves listeners better than a curated hierarchy, or whether we're just avoiding the hard work of classification by calling it "emergent." I suspect the answer changes as the library scales. Something to watch.
This has been My Weird Prompts. Thanks to our producer Hilbert Flumingtop. If you enjoyed this episode, leave us a review wherever you listen — it helps people find the show.
I'm Corn.
I'm Herman Poppleberry. We'll catch you next time.
