DEV Community: Jeffrey.Feillp

You're Not Using Enough Guardrails — Here's What Actually Works (1788023293263)

Jeffrey.Feillp — Sat, 29 Aug 2026 17:08:13 +0000

Everyone talks about AI guardrails. Most of them check the wrong thing.

Input guardrails vs output guardrails

Most guardrail solutions (content filters, prompt injection detectors, topic classifiers) operate on the input — what the user asks. They prevent bad prompts from reaching the model.

But the real cost comes from output problems — the model confidently returning wrong answers that pass all input filters.

What output guardrails should catch

Fabricated citations — the model makes up sources
Hallucinated tool arguments — wrong parameters passed to APIs
System prompt leakage — internal instructions leaked in output
Safety refusal bypass — the model says "I can't answer" but then answers anyway
Code with logic errors — syntactically valid but semantically wrong

A different approach

Instead of filtering before generation, I built a layer that filters after generation. It uses 13 detectors across 5 categories, with 31 correction strategies that fix issues automatically. If a correction is ambiguous, it flags the output for human review.

The key insight: post-generation verification catches things pre-generation filters miss, because you're checking the actual output, not guessing what the model might produce.

Download: https://agent-download-site.vercel.app

Free, model-agnostic, CPU-only.

Why Your AI Agent Keeps Gaslighting You (And How to Fix It) (1788016075521)

Jeffrey.Feillp — Sat, 29 Aug 2026 15:07:56 +0000

You ask your AI agent to check a function. It comes back with a detailed analysis. You trust it. You deploy. The system breaks.

This isn't a bug. It's a feature of how LLMs work.

The gaslighting problem

LLMs are trained to maximize plausible-sounding completions. When they don't know something, they don't say "I don't know" — they invent. And because the output is structured like a confident answer, you believe it.

I've seen agents:

Fabricate API responses that looked real
Invent file paths that don't exist
Claim to have edited code when they didn't
Make up academic citations

The pattern

Agent is uncertain → fills gap with plausible content
Content sounds confident → human trusts it
Content is wrong → human blames themselves
Repeat

Breaking the loop

The fix isn't to trust the model more. It's to verify the output before it matters.

I built a verification layer that runs after every agent output. It checks for fabricated citations, invalid code, tool call hallucinations, and coherence with the original prompt. If something fails, the output gets corrected or flagged before it reaches you.

Result: hallucination rate dropped from ~18% to under 3%.

Download: https://agent-download-site.vercel.app

Free, model-agnostic, runs on CPU. No GPU needed.

I Tracked Every AI Hallucination for a Week — The Numbers Were Worse Than I Thought (1788008814568)

Jeffrey.Feillp — Sat, 29 Aug 2026 13:06:55 +0000

Last week I ran an experiment. Every time my AI agent generated an output, I verified it manually and logged whether it was correct.

The results were embarrassing.

Out of 200 outputs across Claude, GPT, and DeepSeek:

36 were confidently wrong (18%)
12 fabricated citations or references
8 tried to use tools with hallucinated arguments
4 leaked system prompt content

That's nearly a fifth of my token budget going to outputs I had to manually catch and redo.

Why this happens

LLMs are optimized to sound convincing, not to be correct. When they hit uncertainty, they fill gaps with plausible-looking content. The problem is that plausible != true, and in code, "plausible but wrong" costs hours to debug.

What I built

A verification layer that sits between the model and your workspace. It runs after generation but before the output reaches your codebase:

Citation checker — validates references against actual sources
Code validator — checks syntax and logical consistency
Safety leak detector — catches leaked system prompts
Argument verifier — checks tool call parameters against schemas
Coherence scorer — compares output against the original prompt

All runs in under 100ms on CPU. Model-agnostic. Free.

Download: https://agent-download-site.vercel.app

Try auditing your own agent's outputs for a day. You might be surprised what you find.

You're Not Using Enough Guardrails — Here's What Actually Works (1788001277818)

Jeffrey.Feillp — Sat, 29 Aug 2026 11:01:18 +0000

Everyone talks about AI guardrails. Most of them check the wrong thing.

Input guardrails vs output guardrails

Most guardrail solutions (content filters, prompt injection detectors, topic classifiers) operate on the input — what the user asks. They prevent bad prompts from reaching the model.

But the real cost comes from output problems — the model confidently returning wrong answers that pass all input filters.

What output guardrails should catch

Fabricated citations — the model makes up sources
Hallucinated tool arguments — wrong parameters passed to APIs
System prompt leakage — internal instructions leaked in output
Safety refusal bypass — the model says "I can't answer" but then answers anyway
Code with logic errors — syntactically valid but semantically wrong

A different approach

The key insight: post-generation verification catches things pre-generation filters miss, because you're checking the actual output, not guessing what the model might produce.

Download: https://agent-download-site.vercel.app

Free, model-agnostic, CPU-only.

Why Your AI Agent Keeps Gaslighting You (And How to Fix It) (1787993950571)

Jeffrey.Feillp — Sat, 29 Aug 2026 08:59:11 +0000

You ask your AI agent to check a function. It comes back with a detailed analysis. You trust it. You deploy. The system breaks.

This isn't a bug. It's a feature of how LLMs work.

The gaslighting problem

I've seen agents:

Fabricate API responses that looked real
Invent file paths that don't exist
Claim to have edited code when they didn't
Make up academic citations

The pattern

Agent is uncertain → fills gap with plausible content
Content sounds confident → human trusts it
Content is wrong → human blames themselves
Repeat

Breaking the loop

The fix isn't to trust the model more. It's to verify the output before it matters.

Result: hallucination rate dropped from ~18% to under 3%.

Download: https://agent-download-site.vercel.app

Free, model-agnostic, runs on CPU. No GPU needed.

I Tracked Every AI Hallucination for a Week — The Numbers Were Worse Than I Thought (1787986730176)

Jeffrey.Feillp — Sat, 29 Aug 2026 06:58:55 +0000

Last week I ran an experiment. Every time my AI agent generated an output, I verified it manually and logged whether it was correct.

The results were embarrassing.

Out of 200 outputs across Claude, GPT, and DeepSeek:

36 were confidently wrong (18%)
12 fabricated citations or references
8 tried to use tools with hallucinated arguments
4 leaked system prompt content

That's nearly a fifth of my token budget going to outputs I had to manually catch and redo.

Why this happens

What I built

A verification layer that sits between the model and your workspace. It runs after generation but before the output reaches your codebase:

Citation checker — validates references against actual sources
Code validator — checks syntax and logical consistency
Safety leak detector — catches leaked system prompts
Argument verifier — checks tool call parameters against schemas
Coherence scorer — compares output against the original prompt

All runs in under 100ms on CPU. Model-agnostic. Free.

Download: https://agent-download-site.vercel.app

Try auditing your own agent's outputs for a day. You might be surprised what you find.

You're Not Using Enough Guardrails — Here's What Actually Works (1787979468392)

Jeffrey.Feillp — Sat, 29 Aug 2026 04:57:49 +0000

Everyone talks about AI guardrails. Most of them check the wrong thing.

Input guardrails vs output guardrails

Most guardrail solutions (content filters, prompt injection detectors, topic classifiers) operate on the input — what the user asks. They prevent bad prompts from reaching the model.

But the real cost comes from output problems — the model confidently returning wrong answers that pass all input filters.

What output guardrails should catch

Fabricated citations — the model makes up sources
Hallucinated tool arguments — wrong parameters passed to APIs
System prompt leakage — internal instructions leaked in output
Safety refusal bypass — the model says "I can't answer" but then answers anyway
Code with logic errors — syntactically valid but semantically wrong

A different approach

The key insight: post-generation verification catches things pre-generation filters miss, because you're checking the actual output, not guessing what the model might produce.

Download: https://agent-download-site.vercel.app

Free, model-agnostic, CPU-only.

Why Your AI Agent Keeps Gaslighting You (And How to Fix It) (1787965025352)

Jeffrey.Feillp — Sat, 29 Aug 2026 00:57:06 +0000

You ask your AI agent to check a function. It comes back with a detailed analysis. You trust it. You deploy. The system breaks.

This isn't a bug. It's a feature of how LLMs work.

The gaslighting problem

I've seen agents:

Fabricate API responses that looked real
Invent file paths that don't exist
Claim to have edited code when they didn't
Make up academic citations

The pattern

Agent is uncertain → fills gap with plausible content
Content sounds confident → human trusts it
Content is wrong → human blames themselves
Repeat

Breaking the loop

The fix isn't to trust the model more. It's to verify the output before it matters.

Result: hallucination rate dropped from ~18% to under 3%.

Download: https://agent-download-site.vercel.app

Free, model-agnostic, runs on CPU. No GPU needed.

I Tracked Every AI Hallucination for a Week — The Numbers Were Worse Than I Thought (1787957743895)

Jeffrey.Feillp — Fri, 28 Aug 2026 22:55:44 +0000

Last week I ran an experiment. Every time my AI agent generated an output, I verified it manually and logged whether it was correct.

The results were embarrassing.

Out of 200 outputs across Claude, GPT, and DeepSeek:

36 were confidently wrong (18%)
12 fabricated citations or references
8 tried to use tools with hallucinated arguments
4 leaked system prompt content

That's nearly a fifth of my token budget going to outputs I had to manually catch and redo.

Why this happens

What I built

A verification layer that sits between the model and your workspace. It runs after generation but before the output reaches your codebase:

Citation checker — validates references against actual sources
Code validator — checks syntax and logical consistency
Safety leak detector — catches leaked system prompts
Argument verifier — checks tool call parameters against schemas
Coherence scorer — compares output against the original prompt

All runs in under 100ms on CPU. Model-agnostic. Free.

Download: https://agent-download-site.vercel.app

Try auditing your own agent's outputs for a day. You might be surprised what you find.

You're Not Using Enough Guardrails — Here's What Actually Works (1787950502980)

Jeffrey.Feillp — Fri, 28 Aug 2026 20:55:04 +0000

Everyone talks about AI guardrails. Most of them check the wrong thing.

Input guardrails vs output guardrails

Most guardrail solutions (content filters, prompt injection detectors, topic classifiers) operate on the input — what the user asks. They prevent bad prompts from reaching the model.

But the real cost comes from output problems — the model confidently returning wrong answers that pass all input filters.

What output guardrails should catch

Fabricated citations — the model makes up sources
Hallucinated tool arguments — wrong parameters passed to APIs
System prompt leakage — internal instructions leaked in output
Safety refusal bypass — the model says "I can't answer" but then answers anyway
Code with logic errors — syntactically valid but semantically wrong

A different approach

The key insight: post-generation verification catches things pre-generation filters miss, because you're checking the actual output, not guessing what the model might produce.

Download: https://agent-download-site.vercel.app

Free, model-agnostic, CPU-only.

Why Your AI Agent Keeps Gaslighting You (And How to Fix It) (1787943292236)

Jeffrey.Feillp — Fri, 28 Aug 2026 18:54:53 +0000

You ask your AI agent to check a function. It comes back with a detailed analysis. You trust it. You deploy. The system breaks.

This isn't a bug. It's a feature of how LLMs work.

The gaslighting problem

I've seen agents:

Fabricate API responses that looked real
Invent file paths that don't exist
Claim to have edited code when they didn't
Make up academic citations

The pattern

Agent is uncertain → fills gap with plausible content
Content sounds confident → human trusts it
Content is wrong → human blames themselves
Repeat

Breaking the loop

The fix isn't to trust the model more. It's to verify the output before it matters.

Result: hallucination rate dropped from ~18% to under 3%.

Download: https://agent-download-site.vercel.app

Free, model-agnostic, runs on CPU. No GPU needed.

I Tracked Every AI Hallucination for a Week — The Numbers Were Worse Than I Thought (1787936041448)

Jeffrey.Feillp — Fri, 28 Aug 2026 16:54:02 +0000

Last week I ran an experiment. Every time my AI agent generated an output, I verified it manually and logged whether it was correct.

The results were embarrassing.

Out of 200 outputs across Claude, GPT, and DeepSeek:

36 were confidently wrong (18%)
12 fabricated citations or references
8 tried to use tools with hallucinated arguments
4 leaked system prompt content

That's nearly a fifth of my token budget going to outputs I had to manually catch and redo.

Why this happens

What I built

A verification layer that sits between the model and your workspace. It runs after generation but before the output reaches your codebase:

Citation checker — validates references against actual sources
Code validator — checks syntax and logical consistency
Safety leak detector — catches leaked system prompts
Argument verifier — checks tool call parameters against schemas
Coherence scorer — compares output against the original prompt

All runs in under 100ms on CPU. Model-agnostic. Free.

Download: https://agent-download-site.vercel.app

Try auditing your own agent's outputs for a day. You might be surprised what you find.