You’ve seen the Large Language Model (LLM) quality improve.
Your team has access to GPT-4, Claude 3.5, Mistral, or something custom.
Internal demos work.
But hallucinations?
They’re still very much around—often showing up when the system hits production.
According to ICONIQ’s 2025 State of AI Report, 38% of AI product leaders rank hallucination among their top three challenges when deploying AI models in customer-facing products. That puts it ahead of increasing compute costs, security concerns, and even talent shortages.
So why is that?
The core problem is deployment. General-purpose models are used in high-context environments without domain adaptation. Validation steps are skipped. Confidence signals are missing. Teams route generated output into workflows without guardrails.
That’s where hallucinations surface and where they start to do damage.
This blog post outlines what high-performing teams can do to contain hallucinations and build AI systems that meet real operational standards.
There’s a Trust Gap in AI Deployment
Your model hits 90% accuracy in testing. On paper, that looks good. But once it’s in production, it might still draft an email with a fake customer name, suggest the wrong legal clause, or quote an outdated financial figure from two quarters ago.
When that happens, the issue isn’t only that the model made an error. It’s that the error occurs in a context where users expect reliability. Their trust in the system gets damaged if the model’s common metrics like accuracy or BLEU scores look strong.
testing Gap in AI Deployment
These metrics measure performance in aggregate across a broad test set. They tell you how the model performs on average. But users don’t experience averages. They experience specific outputs in real time.
For instance, one Fortune 100 insurance company tested an AI assistant to draft policy summaries. The model passed accuracy benchmarks in staging. But in production, it invented a clause that didn’t exist in the source documents.
Legal blocked the rollout within two weeks. Not because the model was wrong most of the time but because it was unpredictable where it mattered.
In software testing, trust breaks the same way.
If your AI testing process isn’t connected to your real product context, the results can look fine on paper but fail where it matters. CoTester 2.0 was built for this exact challenge.
It learns from your actual stories, specifications, and UI so the tests you run in production reflect the reality your users see.
The Root Cause of Hallucinations: Generals Models in Specific Domains
Why do these trust-breaking errors happen in the first place?
Because most teams deploy general-purpose LLMs, trained on broad internet-scale data, into high-context, domain-specific workflows without adapting them. The model knows language patterns. But it doesn’t know your proprietary datasets, regulatory constraints, or edge cases.
The model has never been taught how your acronyms differ from industry norms, which client names are off-limits, or what “Q4 reserves” mean in your specific context. So even if it scores well on the metrics discussed earlier, it isn’t tested for domain alignment.
The result?
Fabricated entities, misinterpreted jargon, and stale data retrieval. Hallucination occurs when there’s a mismatch between a general model’s training scope and the specialized environment you expect it to operate in.
CoTester 2.0 works from the other direction. It starts with your environment and your data, then adapts its testing logic to match. When your product changes, its AgentRx self-healing engine updates tests during execution so they stay aligned, even through major UI shifts.
Hallucination Tolerance Varies by Industry
What to Do to Contain Hallucinations (And Build Trust at Scale)
If you’re serious about deploying AI in high-risk, high-precision environments, you need systems that reflect how your business operates. The teams that get this right don’t obsess over accuracy percentages or BLEU scores in isolation.
Four practices to contain AI hallucinations - infographic
They design workflows that handle uncertainty, surface edge cases, and build trust over time. Here’s how to do it right.
1. Train for context, not generality
Start by fine-tuning or grounding the model with your own data. That includes support tickets, internal documentation, product manuals, policy memos, and CRM logs. Remove anything that isn’t final, approved, or current, such as outdated SOPs, incomplete drafts, or duplicated T&Cs.
Once you’ve assembled the data, add structure. Index your filtered source material using a retrieval layer and follow best practices for pre-processing:
- Convert PDFs and scanned docs to clean, parseable text
- Flatten inconsistent formats (e.g. title styles, bullet structures)
- Strip out headers and footers that introduce irrelevant tokens
- Use a vector database with metadata filters to pull the most relevant document snippets based on task type, geography, or product. Set up tags like below to reduce drift:
region=Canada, product=Prepaid, status=Active
channel=Retail, document_type=Policy, effective_date=2024-12-01
Next, tighten the prompt layer. Here, don’t rely on generic instructions. Instead, use examples from your real workflows. Pull prompts directly from past agent interactions or internal forms. Match the format and language your teams use every day.
Constrain the output wherever you can: define formats, enforce response structures, and set boundaries on tone. Narrowing the output space minimizes guessing, and with it, hallucinations.
Therefore, start with:
- Max 3–4 retrieved passages
- Each chunk no longer than 300–500 tokens
- Minimum cosine similarity or match score >0.8
- Topic overlap thresholds, if available Review the worst outputs in your logs. If they contain more context than necessary, your chunking or match logic is likely too loose.
Finally, review your edge cases. Feed failed generations and escalations back into your training loop. These show you exactly where your system lacks context and where new guardrails are needed.
CoTester 2.0 applies the same principles to testing. It connects directly to JIRA, documentation, and live UI states, indexing them with metadata so every test run pulls the most relevant context.
Built-in guardrails pause the process at key points for your review, giving you the certainty that each step matches the intent of your workflows.
2. Build confidence scoring into every interaction
Once your model is trained or grounded, your next task is to monitor what it produces. Track confidence at the token level if possible. If you’re using retrieval, blend that with the relevance score from your search layer for a composite reliability signal.
Then, define thresholds that match the risk of the workflow.
For example, for internal drafts, you might accept anything above 85%. For customer-facing communication, you may want 92% or higher. For anything involving financial disclosures or legal recommendations, push it to 98% and add a human reviewer.
Route low-confidence outputs into review queues, not as failures but as high-value QA signals.
For instance, if a claims processing model repeatedly flags policy summaries as “low confidence,” auditing those cases might reveal that certain policy templates or phrasing confuse the model. That insight reveals exactly which prompts or workflows need tightening.
In testing, this kind of checkpoint is built into CoTester 2.0. Before executing a test, it confirms direction at critical points, ensuring nothing proceeds without your approval. The result is a more reliable process and fewer surprises when your code reaches production.
In addition, track retrieval misses and recovery behavior. Reveal gaps in:
- Indexed content
- Weak metadata filters
- Workflows that need a default or human fallback Set a timeout or fallback condition. If retrieval doesn’t return anything with a high enough match score, don’t let the model generate a blind response. Escalate it or surface a fallback response.
3. Add human oversight where it matters
No matter how well you train or score the model, some responses will fall into the grey zone. That’s where human review comes in. What you can do is set up automated triggers for review.
Start with confidence thresholds. Anything that falls below your benchmark should be flagged for manual approval. Then, add rule-based flags for false financial values and legal clauses, sensitive keywords, or unverified citations.
Make sure reviewers have what they need. Build interfaces that show the original input, the model’s output, the flagged risk, and the recommended fallback options so that they can act quickly. Moreover, every reviewer decision should feed back into the system.
When someone corrects a model’s output or rejects it outright, log the context and use it to improve your prompts, retrain your model, and adjust the retrieval layer. That’s how you scale trust without scaling review costs.
4. Pick internal use cases first
Before you put an LLM in front of customers, test it in environments where mistakes are recoverable. That starts with internal use cases, which are structured and high-volume workflows: helpdesk automation, operations documentation, and HR knowledge retrieval.
Deploy the model in shadow mode first. Let teams see the output, but not act on it. Track what they ignore, what they correct, and what they escalate. Then phase into production gradually, from suggestions to approvals to automation, depending on risk tolerance.
Instrument everything: capture rejection rates, edit frequency, and escalation trends. Review these signals weekly with your team. This is where you’ll uncover prompt weaknesses, domain gaps, and system issues long before external users do.
The Finish Line Isn’t Output. It’s Reliable Deployment.
Shipping an LLM into production isn’t the hard part. Making it reliable is.
You can integrate a model in a week.
Run a demo in an afternoon.
But if your system isn’t capable of managing uncertainty, controlling risk, or routing exceptions, you’re not ready to deploy. What you need is fewer generalized models, more domain-specific training, retrieval that reflects real business logic, and review layers that are part of the pipeline.
So take hallucinations seriously. But don’t stop at detection. Design for containment. Audit for recurrence. Build for trust.
This blog is originally published at Testgrid : Why Hallucinations Still Break AI in Production (And What to Do Differently)
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