You ever find yourself pacing the kitchen at three in the morning, one arm occupied by a tiny human who refuses to acknowledge the concept of sleep, trying to Google "how to soothe a teething nine-month-old" with your one free thumb? It is the ultimate stress test for user interface design.
It really is. The physical constraints of parenting are basically the "extreme environment" that engineers should be testing every consumer product in. If you can't use it while sleep-deprived and one-handed, is it even a finished product?
And today’s prompt from Daniel hits on that exact scenario. He’s been working on a parenting advice agent—specifically a voice-based one—because when you’re Daniel, pacing around in Jerusalem with young Ezra, you don’t have a spare hand for a keyboard. But he’s running into a classic engineering headache: the "who am I" problem. He doesn't want to tell the AI his name is Daniel and his son is Ezra every single time he asks a question.
It’s the "colloquial context" problem. By the way, I should mention, today’s episode is powered by Google Gemini three Flash. Fitting, since Daniel mentioned he’s been leaning on the Flash models for their speed and cost-effectiveness. But back to the context issue—we aren't talking about a massive database of documents here. We’re talking about three or four sentences of foundational identity that make the AI actually useful. I’m Herman Poppleberry, and today we are digging into the nuts and bolts of how you actually store those small, persistent details without making your code a complete mess.
It feels like such a tiny problem on the surface, right? It’s just a few strings of text. But as Daniel pointed out, we don't have a "standardized" way to do this yet. In the old world, you’d just throw it in a user profile column in a database. In the AI world, you have to decide exactly how and when to whisper that information into the model's ear so it doesn't forget who it's talking to.
And that’s the tension. Do you stuff it into the system prompt? Do you tack it onto every message? Or do you build a tiny little side-car database just for "Daniel’s Life Facts"? Each one of those has a trade-off that affects latency, cost, and how "smart" the agent actually feels.
Let’s start with what Daniel called the "clunky" method—the Fat System Prompt. This is where you just take your instructions, like "You are a helpful parenting assistant," and then you just keep typing. "The user is Daniel. His son is Ezra. Ezra is nine months old. They live in Jerusalem. Daniel likes evidence-based medical advice but also traditional Irish lullabies." Eventually, your "system instructions" look like a biography.
The Fat System Prompt is the "lazy developer" default, and I say that with affection because I do it all the time. The upside is zero infrastructure. You don't need a database; you don't need a lookup step. It’s just one big string sent to the API. But the downside is what I call "instruction dilution." When you mix "who the user is" with "how the AI should behave," the model starts to get confused about priorities.
Is that still a big issue? I mean, with Gemini one point five Flash—especially the updates from January twenty twenty-six—the instruction-following is significantly better. Can’t these models handle a few extra sentences of bio without losing the plot?
They can handle it better than they used to, but you’re still wasting tokens. Think about it: if you have a hundred users, and each one has a slightly different "Fat System Prompt," you can't cache that system prompt as effectively. You’re paying to send those same biographical details over and over again in the "instruction" block, which is usually weighted differently by the model's attention mechanism than the actual conversation.
Plus, it’s just bad data hygiene. If Ezra turns ten months old—which, spoiler alert for Daniel, happens surprisingly fast—you have to go into your code or your prompt management system and manually edit the "instructions" for that specific user. That doesn’t scale. It’s like hard-coding your grocery list into the source code of your fridge.
That’s a great way to put it. And there’s a psychological component for the model too. If the system prompt says "You are an expert in Jerusalem-based parenting," the model might start hallucinating that it only knows about Jerusalem, even when Daniel asks a general question about sleep cycles. It narrows the "persona" in ways you might not intend.
So if the system prompt is the wrong bucket, what about the "Pre-pending" method? This is where the system prompt stays clean—just the rules of the road—but every time the user sends a message, the app secretly tacks the context onto the front. Like, Daniel says "What’s a good breakfast for a nine-month-old?" and the backend turns that into "Context: User is Daniel, son is Ezra, nine months. Question: What’s a good breakfast?"
This is actually what a lot of production agents do in the early stages. The pro here is that it’s model-agnostic. You can swap from Gemini to Mistral to OpenAI, and your "context injection" logic stays exactly the same. It’s explicit. The model sees the context right before the question, so it’s very high in the "priority" list of the context window.
But isn't that even more token-heavy? If I have a twenty-message back-and-forth, am I sending "Ezra is nine months old" twenty times?
It’s redundant. If you’re using a model with a huge context window, you’re basically paying a "context tax" on every single turn of the conversation. And if you’re building a voice agent like Daniel’s, latency is king. If your prompt gets twenty percent longer because of injected context, the "time to first token" might crawl up just enough to make the conversation feel laggy. When you’re holding a crying baby, a three-second delay feels like an eternity.
I’ve noticed that with voice agents. If there’s even a beat of silence after you finish talking, you start wondering if the app crashed. It ruins the illusion of "agentic" behavior. It just feels like a slow computer. So, if we hate the Fat System Prompt for being messy, and we hate Pre-pending for being expensive and slow, what’s the "engineer’s choice"?
The middle ground is a lightweight key-value store. We’re talking about something as simple as a JSON file or a SQLite database. Instead of trying to force the LLM to "remember" these facts through the prompt, you treat the facts as data that gets pulled in only when needed.
But Daniel’s point was that he doesn't want to set up a whole vector database for three sentences. Is SQLite overkill for just knowing a kid's name?
Not at all. In twenty twenty-six, running a local SQLite instance is practically free in terms of overhead. The beauty of a key-value store is that it separates "Identity" from "Logic." You have a table for "User Context." When Daniel starts a session, the app pulls "Daniel, Ezra, nine months, Jerusalem" into a local variable.
Okay, but you still have to put that variable somewhere in the prompt to make the AI aware of it. Aren't we back to square one? Whether it comes from a database or a hard-coded string, it still has to enter the LLM's brain.
Not necessarily. This is where the "Agentic" part comes in. If you’re using a framework like LangGraph or CrewAI—or even just a well-structured tool-calling setup—you don't have to give the LLM the context upfront. You give the LLM a tool called "get_user_context."
Oh, I like that. So the AI starts the conversation with a clean slate. Daniel says "He won't stop crying," and the AI thinks, "Wait, I don't know who 'he' is or how old he is. Let me check the user context tool." It hits the SQLite database, sees "Ezra, nine months," and then responds with "Since Ezra is nine months, it could be teething..."
That is the "clean" way to build an agent. It keeps the context window lean. It only pulls the data when it's actually relevant. If Daniel asks a question about the weather in Tel Aviv, the agent doesn't need to waste tokens thinking about Ezra’s age. It only invokes the memory when there’s a "missing variable" in the user’s request.
That feels much more like how a human brain works. I don't walk around constantly reciting my brother’s age and address in my head. I only pull that info up when I’m buying you a birthday card or calling you an Uber. But let's talk about the "parenting in the kitchen" test. If the agent has to "think" about calling a tool, then call the tool, then get the result, then generate an answer... isn't that slower than just having the text already there in the prompt?
It’s a trade-off. Tool-calling adds a round trip. But with Gemini three Flash, that round trip is incredibly fast—we're talking milliseconds. The real benefit is the reliability. When you explicitly pull context from a data store, you're less likely to get "hallucinated context."
I’ve seen that happen. You tell an AI too much about yourself in the system prompt, and it starts assuming everything you say is related to that. You ask "What’s the capital of France?" and it says "The capital of France is Paris, which is a great place to take a nine-month-old like Ezra!" It becomes that annoying friend who can only talk about one thing.
We call that "attention drift." The model's attention mechanism gets so saturated with the "foundational context" that it starts to bleed into the actual task. By moving that context into a side-car database and using tool-calling or a "retrieval-on-start" pattern, you keep the conversational lane clear.
Let’s look at the "retrieval-on-start" pattern. That sounds like a hybrid. When the voice app wakes up, it does one quick fetch to the database: "Who is this?" It gets the bio, and then it populates the session memory. So for the rest of that specific kitchen-pacing session, the info is in the context window, but it’s not "hard-coded" into the system.
You treat it as "ephemeral session memory" derived from "persistent data." When the session ends, the context window is wiped. If Ezra has a birthday tomorrow, you update the SQLite database once, and the next time the agent wakes up, it pulls the new age. No prompt engineering required.
This seems especially important for the "multi-user" scenario Daniel mentioned. If you're building a startup, you can’t have a thousand different system prompts. You have one system prompt that says "You are a parenting expert. Use the provided context to tailor your advice." Then your backend handles the "injection" of Daniel’s specific life or Herman’s specific life.
And that’s where the "colloquial context" becomes an engineering asset rather than a chore. If you store it as structured data—like a JSON object with keys for 'user_name', 'child_name', 'location'—you can actually do logic on it before it even hits the AI. You could have a bit of code that calculates Ezra’s age in weeks automatically. The LLM doesn't have to do math—which it’s historically "meh" at—it just receives the current, accurate facts.
"Historically meh" is a very polite way of saying LLMs used to think nine months plus one month equals eleven. They’ve gotten better, but your point stands. Don't make the AI do work that a three-line Python script can do perfectly every time.
Right. So for Daniel, pacing the kitchen, the "pro" move isn't a better prompt. It’s a better "orchestration layer." He mentioned Gumloop and LangGraph—those tools are designed for exactly this. You create a "pre-flight" node in your workflow. Step one: Fetch User Bio. Step two: Check for Recent Events. Step three: Send to LLM.
I want to push back on the "don't use a vector database" thing for a second. I know Daniel said it's overkill for three sentences. But what if those three sentences grow? Today it’s "Ezra is nine months." In six months, it’s "Ezra likes blueberries, he’s allergic to peanuts, he’s hitting his crawling milestones." Suddenly, your "foundational context" is a whole page of text. Does the SQLite key-value store still hold up?
It does, because you can still use basic keyword search. You don't need "semantic vector embeddings" to find a peanut allergy. You just need a table of 'Facts.' If the user mentions 'food,' you query the 'Facts' table for anything tagged 'food' or 'allergy.' It’s "RAG-lite."
RAG-lite. I like that. It’s like the diet soda of AI architectures. All the flavor of retrieval, none of the "I have to manage a Pinecone index" calories.
Precisely. And for a voice agent, that simplicity is your best friend. Every layer of complexity you add—vector databases, embedding models, semantic rerankers—is another place for the system to break or lag. If Daniel is asking the agent "Can he have honey yet?", he needs an answer now. He doesn't need a semantic search of his entire life history. He just needs the "Age" variable and the "Medical Preferences" variable.
It’s funny, the more we talk about "agentic" tools, the more it sounds like traditional software engineering. It’s about state management. We spent the last three years obsessing over "prompts," but in twenty twenty-six, the prompt is just the "UI" for the model. The real "engine" is how you manage the data flowing into that UI.
That’s the shift. We’re moving from "Prompt Engineering" to "Context Orchestration." Daniel’s frustration with the "clunky system prompt" is a sign that he’s outgrown the "Chatbot" phase and moved into the "Agent" phase. A chatbot listens and responds. An agent knows and acts. And "knowing" requires a stable place to keep its knowledge that isn't just a giant, messy paragraph of text.
Let’s talk about the "Voice" specific constraints of this. When you’re using a voice interface, you don't have a "chat history" you can scroll back through to see where the AI got confused. If the agent forgets Ezra's name halfway through a conversation, it feels like a personal betrayal.
It breaks the social contract. If I’m talking to a human about my kid, and five minutes later they say "So, how is your daughter doing?", I’m immediately checking out of that conversation. With AI, that "betrayal" happens because of context window truncation. The conversation gets too long, the oldest messages—the ones where you said "My son Ezra"—get pushed out of the window, and the AI loses its "foundational context."
And that’s the strongest argument for the "Side-car Database" or "Tool-calling" approach. If the info is in the system prompt or a pre-pended block, it’s "at risk" of being ignored or diluted in a long conversation. If it’s a "Tool" the agent can call, it’s always available, no matter how long the transcript gets.
It’s like giving the AI a permanent "cheat sheet" it can glance at whenever it needs to. "Oh, right, Ezra. Nine months. Jerusalem." It makes the agent feel much more stable and "present."
So, if we’re giving Daniel a "to-do list" for his parenting agent, where does he start? He’s already got the prototype. He’s using Gemini three Flash. He’s got the basic logic. How does he transition from "clunky prompt" to "elegant agent"?
Step one: Strip the bio out of the system prompt. Keep the system prompt focused entirely on how to speak. "You are a concise, evidence-based parenting assistant. Use the provided tools to verify child age and medical history before giving safety advice."
Step two: Set up the "Identity Store." If he’s building this in a tool like Gumloop or a custom Python script, just use a simple JSON object or a SQLite row.
Right. And Step three: The "Injection Strategy." For his specific "one-user" case, the "Pre-pended Context" actually isn't a bad move if he wants to stay low-code. Just have the app start every prompt with "Current Context: [JSON Object]." It’s a bit of a token waste, but for a personal project, it’s the most reliable way to ensure the model never "forgets" the basics.
But if he wants to go "Pro," he should look at the "Injected System Instruction" feature that models like Gemini have been leaning into. You can actually update the "System Instructions" mid-conversation now. So when the app starts, it fetches the bio and sets that as the system instruction for the session. It’s cleaner than pre-pending it to every message.
That’s a great point. It keeps the "User Message" lane purely for Daniel’s questions. It separates the "Facts" into the "Instruction" layer, where the model's attention is more focused on the "rules of engagement."
I think there’s also a "Takeaway" here for people who aren't building parenting agents. This problem of "Sparse Foundational Context" is everywhere. It’s in coding assistants who need to know your preferred libraries. It’s in travel agents who need to know your frequent flyer number. It’s the "Small Data" problem of AI.
We’ve spent so much time on "Big Data"—RAG with millions of documents—that we forgot how to handle "Small Data." But for a truly personal agent, the Small Data is actually more important. The fact that my son is allergic to peanuts is a "Small Data" point, but it’s more critical than any "Big Data" medical journal entry about peanut allergies in general.
It’s the difference between "Knowledge" and "Context." Knowledge is knowing that babies shouldn't have honey. Context is knowing that there is a specific baby in the room right now who is under one year old. Without the context, the knowledge is just a textbook.
And that’s the goal of "Agentic Engineering." We’re trying to build textbooks that know who’s reading them. Daniel’s approach—using those lightweight models like Flash—is exactly right because it gives you the "headroom" to do these extra context-management steps without breaking the bank or waiting five seconds for a response.
It’s also worth noting that as these models get cheaper—and they are getting insanely cheap—the "token tax" of sending a bit of extra context matters less and less. We’re moving into an era of "Token Abundance." So if you’re a developer and you’re stressed about wasting fifty tokens on a bio... maybe just let it go. The user experience is worth more than the fraction of a cent you’re saving.
"Token Abundance" is a dangerous drug, Corn. That’s how you end up with messy, inefficient agents. I still think the discipline of "Context Orchestration" is worth it. Even if tokens are free, the attention of the model is finite. The more junk you put in the prompt, the less the model focuses on the core question.
Fair point. You can have an infinite context window, but you don't have infinite "reasoning power" to apply to that window. The "Lost in the Middle" phenomenon—where models forget things buried in the center of a long prompt—is still a reality, even in twenty twenty-six.
It is. So, keep the "Foundational Context" at the very top or the very bottom. Or, better yet, keep it in a structured block that the model recognizes as "The Facts."
One last thing on the "Voice" front. Daniel mentioned the "hands-tied" scenario. One thing I’ve found useful in those agents is a "Context Confirmation" step. Every once in a while, the agent should say, "Just to confirm, we’re still talking about Ezra, who’s nine months now, right?" It’s a way for the agent to "save" its state and for the user to correct it if the "Small Data" has changed.
That’s brilliant. It turns "State Management" into a conversational feature. "Hey, I noticed it’s been a month since we talked—is Ezra ten months old now?" It makes the agent feel like it’s growing with the family. It moves from "Tool" to "Companion."
And that’s the "Weird Prompt" dream, isn't it? An AI that doesn't just answer questions, but actually understands the life it’s inhabiting. Whether you’re in Jerusalem or anywhere else, having a "Parenting Agent" that knows your kid's name isn't just a convenience—it’s a way to feel a little less alone in that three-a-m kitchen pacing.
It’s about reducing the "cognitive load" of the interface. When you’re stressed, you don't want to "interface" with a computer. You want to talk to something that already understands the situation.
Well, I think we’ve given Daniel enough architectural homework to keep him busy while Ezra naps. We’ve covered the "Fat System Prompt" trap, the "Pre-pending" tax, and the "SQLite Side-car" solution.
And the most important takeaway: don't over-engineer the "Small Data." If a JSON file works, use a JSON file. You don't need a spaceship to cross the street.
Unless the spaceship is really fast and has a built-in bottle warmer. Then I might consider it.
Always looking for the gadgets, Corn.
Guilty. But seriously, this is the frontier. We’re all just figure out how to weave these models into our actual, messy lives. Thanks to Daniel for the prompt—it’s a perfect example of a "Real World" AI problem that doesn't have a "Standardized" answer yet.
It’s the "Hackers' Frontier." And honestly, that’s the most exciting place to be.
Before we wrap up, I want to say thanks to our producer, Hilbert Flumingtop, for keeping the gears turning behind the scenes.
And a big thanks to Modal for providing the GPU credits that power this show and our research. They make it possible for us to dive deep into these models and figure out what actually works.
This has been My Weird Prompts. If you’re finding these deep dives useful, or if you’ve built your own "Small Data" solution for an agent, we want to hear about it. Drop us a review on your favorite podcast app—it really does help other "Agentic Engineers" find the show.
We’ll be back next time to talk more about the next layers of this puzzle—RAG and long-term memory. But for now, get those SQLite databases ready.
And good luck in the kitchen, Daniel. We’re rooting for Ezra to get some sleep.
See ya.
Bye.
