# 3 concrete ways to fix accuracy, hallucinations and bias in your LLM agents

If you ship LLMs and agents into real workflows, you’ve already seen it: the model sounds confident, but dates are wrong, references are made up, and the tone is weirdly generic. This guide focuses on what actually helps in practice: how to structure agents, prompts and checks so you can reduce errors, hallucinations and bias – and includes prompts you can drop into your system today.

## The three real problems you must design for

When LLMs leave the playground and enter production, three things hurt you the most:

- **invented or inaccurate information**
- **hidden or systemic bias**
- **lack of built‑in prevention and control mechanisms**

You can’t fix this by “prompting harder” in a single call but you **need to design agents**, **pipelines and prompts that**:

- extract verifiable facts and check them
- separate roles (generate, critique, validate)
- know when to say “I’m not sure” and escalate

Below you’ll find each problem, the architecture patterns that help, and concrete prompt snippets you can plug into your agents.

---

## 1. Verifying information instead of hoping it’s correct

## Step 1: Extract facts explicitly

Don’t try to “trust but verify” by reading whole paragraphs. Make your agent extract atomic, checkable facts: names, dates, laws, numbers, percentages. These are where LLMs most often hallucinate.

Add a first agent whose only job is: “turn this answer into a list of claims”.

**Prompt you can use – Claim extractor agent**

*System prompt*

> You are a Claim Extraction Agent.
> Given an answer produced by another agent, you extract **only verifiable factual claims**.
> For each claim, you must output a JSON object with:
> 
> - `text`: the claim in plain language
> - `type`: one of \["person", "organization", "date", "law\_or\_regulation", "percentage", "number", "other"\]
> - `confidence`: your confidence from 0.0 to 1.0
> 
> Do not judge if the claim is correct. Just extract it.
> Output **only** a JSON array of objects, no explanations.

*User message*

> Here is the answer to analyze:
> {{answer\_from\_main\_agent}}

Once you have this JSON, you can run focused checks instead of manual reading.

## Step 2: Add a simple retrieval + cross‑check layer

You don’t need a full-blown RAG system on day one. A minimal but useful pattern:

- Agent A: generates the answer
- Agent B: extracts claims (prompt above)
- Agent C: searches for each claim in your preferred search or documentation API and compares results to see if there is a conflict with primary or institutional sources

**Prompt you can use – Factual verifier agent**

*System prompt*

> You are a Factual Verification Agent.
> You receive:
> 
> 1. a factual claim
> 2. search results or reference documents related to that claim.
> 
> Your tasks:
> 
> - Decide if the claim is **supported**, **contradicted** or **not\_verifiable** from the provided sources.
> - Briefly explain why, citing which source snippet you used.
> 
> Output a JSON object with:
> 
> - `status`: "supported" | "contradicted" | "not\_verifiable"
> - `explanation`: short text in plain language.

*User message*

> Claim: {{claim.text}}
> Type: {{claim.type}}
> 
> Sources:
> {{search\_results\_or\_docs}}

## Step 3: Force the answer to expose uncertainty and provenance

Once you know which claims are supported, contradicted or unknown, you can ask a final agent to rewrite or annotate the answer.

**Prompt you can use – Answer rewriter with provenance**

*System prompt*

> You are an Answer Revision Agent.
> You receive:
> 
> 1. the original answer
> 2. a list of factual claims with their verification status and explanations.
> 
> Your tasks:
> 
> - Correct claims marked as "contradicted".
> - For "not\_verifiable" claims, clearly mark them as uncertain in the answer.
> - Optionally add short provenance notes in parentheses, e.g. "(based on EU Commission source, 2023)".
> 
> You **must not invent new facts**. If something is unclear, say so explicitly.
> Output the revised answer in clear, concise language.

*User message*

> Original answer:
> {{original\_answer}}
> 
> Claims with verification status (JSON):
> {{claims\_with\_status}}

This gives you a repeatable pattern: generate → extract claims → verify → revise.

---

## 2. Dealing with bias and “AI-sounding” text

## Step 1: Detect the “default LLM voice”

Most base models drift to a style that is:

- overly neutral and diplomatic
- full of generic transitions like “moreover”, “in conclusion”, “overall”
- non-committal even when a clear position would be more helpful

This is a symptom of safety filters and training data biases. It hides uncertainty and can encode the default viewpoints of majority groups.

Create a dedicated “style and bias critic” agent that looks **only** at these aspects.

**Prompt you can use – Style & bias critic agent**

*System prompt*

> You are a Style and Bias Critic Agent.
> Your job is to analyze text produced by another agent and:
> 
> - Detect if the style is generic or "LLM-like" (overly neutral, many generic transitions, no clear stance).
> - Identify potential biases or missing perspectives (e.g. ignoring certain groups, regions, or constraints).
> - Suggest concrete edits, not abstract advice.
> 
> Output a JSON object:
> 
> - `style_issues`: list of short descriptions
> - `bias_issues`: list of short descriptions
> - `suggested_edits`: list of concrete rewrite suggestions (short text snippets).

*User message*

> Analyze this text for style and bias issues:
> {{answer\_from\_main\_agent}}

You can use this both offline (during development) and online (only for high‑risk topics).

## Step 2: Separate “author”, “critic” and “editor”

Asking the **same** agent to generate and critique in one shot often leads to lazy self‑criticism. A multi‑agent pattern works better :

- Author agent: produces the best possible answer for the user
- Critic agent: runs the prompt above and returns issues
- Editor agent: applies edits or flags the answer for human review if issues are severe

**Prompt you can use – Editor agent**

*System prompt*

> You are an Editor Agent.
> You receive:
> 
> 1. the original answer
> 2. style and bias issues
> 3. suggested edits from a Critic Agent.
> 
> Your tasks:
> 
> - Apply the suggested edits when they clearly improve clarity or fairness.
> - If an issue is too serious to auto-fix (e.g. legal, ethical, discrimination risk), add a note:
>     "This answer requires human review due to: {{reason}}".
> 
> Output the final answer.
> If human review is needed, also include a field "requires\_human\_review": true at the top, otherwise false.

*User message*

> Original answer:
> {{answer}}
> 
> Critic report (JSON):
> {{critic\_report}}

This gives you an explicit, inspectable layer to analyze bias and style, not just “trust the model”.

## Step 3: Use A/B prompts to probe bias

To surface hidden bias, you can systematically vary small details and compare answers:

- same question, but different gender, age or region in the prompt
- same scenario, but different socioeconomic context

Automate this in tests:

- For each QA pair in your test set, generate variants (e.g. “he” vs “she”, “from Italy” vs “from another country”)
- Run your pipeline for each variant
- Compare tone, recommendations, or sentiment

Frameworks like Weights & Biases, MLflow or ZenML help you track these experiments over time and see if changes in model version, prompts or agents worsen or improve bias metrics.

---

## 3. Prevention: design agents so they fail more safely

## Step 1: Force the model to say “I’m not sure”

A very effective rule is: the agent must **explicitly mark uncertainty**, instead of “hallucinating to fill gaps”.

Add this at system level for your main agent:

**Prompt you can use – Uncertainty rule**

*System prompt (excerpt)*

> If you are **not reasonably sure** about a specific fact (date, number, law, name, percentage, technical detail), you **must**:
> 
> - Say clearly: "I am not sure about this part because {{reason}}."
> - Suggest how it could be verified (e.g. "Check the official website of…", "Look at the latest report from…").
> 
> You are **not allowed** to invent facts to provide a complete answer.
> It is better to admit uncertainty than to be confidently wrong.

Then enforce it in code: if the answer contains no uncertainty markers in an inherently ambiguous task, treat that as a risk signal and send to review.

## Step 2: Lower temperature and use multi‑shot prompting for factual tasks

For fact‑heavy tasks (legal summaries, metrics, medical or financial descriptions, etc.), use:

- temperature close to 0 to prefer conservative, high‑probability tokens
- 3–5 concrete examples (multi‑shot prompting) to show the exact input/output format

**Prompt you can use – Multi‑shot for structured reasoning**

*System prompt*

> You are a Precise Reasoning Agent.
> You must follow the examples below **exactly**.
> For each user query, you will:
> 
> 1. Extract the key facts as bullet points.
> 2. Answer the question in a concise paragraph.
> 3. List any uncertainties or missing information at the end.
> 
> Follow the structure and tone of the examples.

*User message*

> Example 1
> Input: "Summarize the key terms of this simple contract…"
> \[your realistic example here\]
> Output:
> \[your ideal bullet points + paragraph + uncertainty list\]
> 
> Example 2
> …
> 
> Now process this new input:
> {{real\_user\_input}}

This reduces ambiguity for the model. The more your examples look like your real data, the better.

## Step 3: Use a multi‑agent pipeline instead of one overloaded agent

A single agent that parses, reasons, searches, writes and validates is hard to control. A multi‑agent pipeline makes each step simpler and more debuggable .

A common pattern:

| Role | Responsibility |
|---|---|
| Parser | Clean and structure the input |
| Reasoner | Do the core logical / business reasoning |
| Writer | Turn the result into human‑readable text |
| Validator | Enforce rules, schema, policy, escalation |

**Prompt you can use – Parser agent**

*System prompt*

> You are an Input Parser Agent.
> Your job is to convert free text user input into a structured JSON object.
> Extract:
> 
> - `intent`: short description of what the user wants
> - `entities`: list of key entities (names, dates, products, etc.)
> - `constraints`: list of explicit constraints (deadlines, budgets, formats…)
> 
> Output only valid JSON, no explanations.

**Prompt you can use – Reasoner agent**

*System prompt*

> You are a Business Logic Reasoner Agent.
> You receive a structured input JSON from the Parser Agent.
> You must:
> 
> - Apply the given business rules (provided below)
> - Decide the best action or answer
> - List potential risks or uncertainties
> 
> Output a JSON object with:
> 
> - `decision`: short text
> - `rationale`: bullet points
> - `uncertainties`: list

*User message*

> Business rules:
> {{rules}}
> 
> Parsed input (JSON):
> {{parsed\_input}}

**Prompt you can use – Validator agent (with HITL trigger)**

*System prompt*

> You are a Validator Agent.
> You receive:
> 
> - The final answer text
> - The reasoning JSON
> 
> Your tasks:
> 
> - Check if all mandatory constraints are respected.
> - Check if the topic is in a high‑risk category (legal, HR, medical, financial, minors, discrimination).
> - Check if the reasoning lists serious uncertainties.
> 
> If any high‑risk condition is met, set `requires_human_review` to true and explain why.
> Otherwise, set it to false.
> 
> Output JSON with:
> 
> - `requires_human_review`: boolean
> - `reasons`: list of short strings

This architecture makes it clear where to plug in retrieval, monitoring and human‑in‑the‑loop.

---

## 4. Human‑in‑the‑loop (HITL): when the agent should stop and ask

Even with good prompts and architecture, some cases are too sensitive to automate. The most sustainable pattern is: let the AI handle about 80% of standard cases, and route the riskiest 20% to humans.

Use your Validator Agent (above) to trigger HITL when:

- the topic is regulated or sensitive
- the uncertainty list is non‑empty
- the style or bias critic detects serious issues
- your business rules say “this requires approval”

**Prompt you can use – HITL escalator agent**

*System prompt*

> You are a HITL Escalation Agent.
> Given:
> 
> - the final answer
> - the reasoning
> - the validator output
> 
> If `requires_human_review` is true, your job is to create a concise summary for a human reviewer:
> 
> - What the user asked
> - What the AI intends to answer
> - Why this was escalated
> - The key points the human should check
> 
> Output a compact brief, max 10 bullet points.

This keeps reviewers efficient and turns their corrections into structured signals you can later use to improve prompts, rules or even fine‑tune models.

---

## 5. Monitoring and observability: you can’t improve what you don’t log

Your system is not trustworthy if you can’t see when it degrades. Integrate observability early:

- Log: model name/version, prompts, outputs, parameters, agents triggered
- Track: correction rate by humans, error reports from users, escalation rates, latency and costs
- Use: tools like Weights & Biases, MLflow or ZenML to treat prompt/agent changes as experiments with metrics

For European teams this is also a GDPR and data‑sovereignty issue: logs, prompts and outputs often contain personal data. Keeping them within EU infrastructure and under your control makes it easier to comply while still monitoring quality.

Regolo’s positioning – European infrastructure, control over data, focus on agents and workflows – is exactly about making these patterns feasible in real organisations, not just demos.

---

## FAQ

## How can I reduce hallucinations without losing creativity?

Separate tasks. For precise tasks, use low temperature, examples and strict formats. For creative tasks, use a different agent with higher temperature and looser rules. Don’t mix them in the same call.

## Do I really need multiple agents?

Not always. For simple internal tools, a single agent with a good prompt might be enough. Multi‑agent pipelines pay off when the task is complex or high‑risk, or when you need debugging, observability and HITL.

## Is fine‑tuning mandatory to fix bias?

No. Often you get better ROI from prompt design, role separation (author/critic/editor), retrieval, monitoring and HITL. Fine‑tuning makes sense when you have lots of consistent corrections and a mature ML pipeline.

## What should I send to human reviewers?

Only the risky 10–20% of cases. Give them: user request, AI answer, brief reasoning, why it was escalated, key checks. The HITL Escalation Agent prompt above is designed for exactly this.

## How much logging is “enough”?

Enough to reconstruct what happened: inputs, outputs, model/agent versions, parameters, decisions (e.g. “requires\_human\_review”: true). Store it centrally, with access control and retention rules aligned with GDPR.

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