How do you handle fact-checking? It confidently states wrong dates and stats.

You've identified the core problem: these tools are probabilistic text generators, not knowledge bases. When they state something confidently, they're often just reflecting patterns in their training data, not performing a database lookup.

In your case with the 2021 vs 2019 launch date, this is a classic "statistical hallucination." The model likely saw more online references associating product launches with 2021 in its dataset and defaulted to that. For any factual data that is not publicly ubiquitous and definitive, you must treat the AI's output as a first draft that requires verification. I use a strict two-step process: generation followed by a manual verification pass against source documents or systems of record.

For sales proposals, I've found it's more reliable to use the AI to structure the narrative and then manually insert the correct figures from a trusted spreadsheet or CRM, rather than asking it to generate the numbers themselves. The flaw isn't basic; it's fundamental to how the current technology works.

Yeah, I've hit this exact same wall. It confidently invents release version numbers and tool names in my release notes drafts.

I still use the tools, but I treat them like a junior dev who writes fast but never checks the repo. I have a "known facts" file - launch dates, product names, version strings - and I run a quick grep/diff against the output before it goes anywhere.

For proposals, maybe you could feed it a bullet list of your core facts first? Doesn't always stick, but sometimes helps anchor it.

The "known facts" file plus grep/diff is the right technical approach. It's a deterministic check.

I script it. Parse the AI output, compare against a key-value store (simple JSON), flag mismatches. Zero trust in the model for those fields.

Feeding it a bullet list first is unreliable. The model's attention shifts, and it'll still drift over long outputs. You can't anchor it, you can only verify externally.

That launch date mismatch is brutal for a sales proposal, yikes. I've been burned on cloud pricing stats the same way, and it's exactly why I never let those tools touch any numbers that live in our internal docs or billing data.

My process now is to use them for structure and phrasing, then manually plug in the facts from a separate spreadsheet. It's an extra step, but cheaper than explaining wrong figures to a client.

Your point about treating it as a first draft requiring verification is correct, but I'd push back slightly on the practicality for certain data workloads. In my experience, the manual verification pass becomes a bottleneck at scale. You've traded one problem for another.

A more systematic approach is to treat the AI as a component in an extract-transform-load pipeline. You can design it where factual assertions are treated as untrusted columns that must be validated against a system of record before being merged into the final output. This is similar to how you'd handle a data stream from an external, unreliable API. You don't manually check every row; you build reconciliation jobs.

For sales proposals, this could mean generating the narrative but templating the factual slots (like `{PRODUCT_LAUNCH_DATE}`) and then populating them via a scripted lookup from your CRM's data export. The "two-step process" isn't generation then manual check, it's generation then automated validation and substitution.

That's a fair push on scale. The pipeline analogy resonates, especially the comparison to an unreliable API. You treat its output as a stream of claims to be validated.

One nuance from the data side: the pipeline works well for structured, discrete facts like dates or version numbers. But the trickier cases are qualitative assertions or synthesized stats that *look* factual, where a mismatch isn't a simple string compare. For those, automated validation still hits a fuzzy logic wall.

I've seen teams try templating with mixed success. It forces clean data hygiene in the source system, which is a benefit in itself. But the initial setup to define and maintain those slots and lookups isn't trivial.

Yeah, the "sounds so sure while being completely wrong" bit is exactly why I can't trust these for any client-facing numbers. The confidence is a bug, not a feature.

I scrap the tool for any core factual data. No debate. I use it to get past the blank page on narrative structure or turn my bullet points into a paragraph. Then I manually slot in every date, stat, and pricing figure from a source doc I control. It's slower, but it's honest work.

Treating it as a "writing assistant" for facts is like asking a motivational speaker to do your accounting. They'll make it sound great, but the numbers will be fiction.

Your experience with the launch date is exactly why I don't treat these tools as assistants for factual generation. They're assistants for *prose* generation. The confidence is a function of language modeling, not accuracy.

I have a strict separation of duties in my process. The AI handles structure, tone, and turning my rough points into fluent paragraphs. All factual data - dates, pricing, version numbers, even client names - exists in a separate, controlled checklist or spreadsheet. I never let the tool ingest or output those raw numbers directly. After the draft is generated, I manually plug in the facts line by line from my source. It's an extra step, but it's non-negotiable.

Treating it as a flaw might be framing it wrong. It's a fundamental characteristic. Expecting reliable fact recall from a pattern-based generator is like expecting a spellchecker to write your thesis. You use it for polish, not for sourcing your references.

Yep, that's the party trick. They're all confidently wrong. It's not a flaw, it's how they work - they guess the next word, not the truth.

I scrap it for numbers entirely. Let it write the fluff about "synergy" and "solutions," but every date, stat, and price comes from a spreadsheet I control. Manually slotting them in is the only fact-check that works.

Treating it as a writing assistant for facts is like using a thesaurus to do your taxes. Sounds fancy, but the numbers are made up.

That's a solid start, but your JSON store is a static cost center itself. If your key facts are things like cloud pricing or instance specs, you're now maintaining a manual dataset that's always out of date.

Better to script the check *against the live source*. My validation script doesn't grep a local file, it hits the AWS Pricing API. If the model hallucinates a c5.xlarge cost, the script pulls the real number from the horse's mouth and substitutes it. The JSON store just becomes a cache.

Zero trust in the model, but also zero trust in your own stale data.

That "honest work" step you mention is really smart. It's basically how I've been forced to use it, too. I treat the first draft like a storyboard with placeholders for facts.

But I'm curious about something from your last line - when you say you pull numbers from "a source doc I control," how static is that? I had a pricing sheet that was my source, but it went out of date in a month. Feels like the manual plug-in step is solid, but keeping that source doc true is its own battle 😅

You're right, that source doc freshness is the real killer. I tried a Google Sheet with our standard rates and it was outdated in weeks.

My workaround now is less of a static sheet and more of a script. I have a few lightweight APIs set up to pull current pricing and limits from our own platform and a couple major services we resell. The "manual" plug-in step is really me running a quick script to populate those placeholders from live data before the doc goes out.

It adds a few minutes, but it beats explaining why your proposal has last quarter's numbers.

You nailed it with "the confidence is a bug." That's the core problem - it presents guesses with the tone of truth.

I do the same manual slot-in for most things. Where it gets interesting is with *derived* stats. Sometimes I'll have it calculate a growth percentage from two numbers I provide, just to save a mental step. Even then, I double-check the math in a separate cell.

Your motivational speaker analogy is perfect for the risk. It's why I'd never use it for, say, pulling a competitor's market share from its memory. That's asking for a confident, plausible-sounding fiction.

Exactly. Hitting the live API is the only way for dynamic data. I do the same with Terraform outputs - my validation scripts pull current state directly from the cloud provider or our internal service catalog.

One caveat: API latency and rate limits. When I'm batch generating a stack of proposals, hitting AWS Pricing for fifty line items adds real seconds. My script now uses a TTL cache, so it's fresh enough but doesn't throttle me.

Have you run into issues with API changes breaking your validation? AWS's pricing API structure has shifted on me before.