Thread by @tyler_agg

how to prevent AI hallucinations (step-by-step breakdown):

everyone's frustrated when their AI gives confident wrong answers

you ask it something simple about your business and it just makes shit up

understand that hallucinations aren't bugs, but more so features you need to manage

most "hallucinations" are actually prompting problems

you give an AI zero context and expect perfect outputs

"write me a proposal" vs "write a proposal for John's marketing agency covering SEO services, timeline is 3 months, budget $5k, he mentioned wanting more organic traffic"

be specific in your prompts and I promise you’ll get much better outputs (shocking I know lol)

your knowledge base sucks

an AI can only work with what you give it

if someone asks about your onboarding process but you never documented it, it’s just going to make shit up

meaning this isn’t an AI problem, it’s a data problem

here's what engineers do: check context relevance first

before an AI answers a request, ask: "does the retrieved information actually relate to this question?"

if no - return "I don't have information about that"

if yes - proceed with answer

now you don’t have to worry about it guessing

build guardrails like any other system

so use a second AI to evaluate answers:

"here's the question, context provided, and AI's answer - does this answer actually match the context and question?"

if it fails the check, don't show the answer

track everything religiously

> what questions are being asked
> what context was retrieved
> what answers were generated
> when you had to say "I don't know"

this data shows you exactly what's missing from your knowledge base

accept that AI models fuck up sometimes

just like APIs timeout, databases crash, and code breaks

the solution isn't to avoid AI - it's to build systems that handle failures gracefully

you need to plan for hallucinations

create your "golden dataset"

collect ideal questions with perfect answers you want

test your AI system against these regularly

if outputs drift from your standards, you know something's broken

the biggest mistake: perfectionism

people want 100% accuracy before using AI

but your human employees make mistakes too

you want to aim for a robust system that accepts perfect is impossible

but can handle any failures and errors when they do come

AI hallucinations decrease when you:

> provide specific context
> check relevance before answering
> use evaluation models as guardrails
> track and fix knowledge gaps
> accept some failures as normal

the key really is just accepting hallucinations happen, but they're manageable