Handling Hallucinations and Accuracy in LLM-Enabled Applications
- Jayant Upadhyaya
- 3 days ago
- 6 min read
Applications have bugs. That has always been true in software engineering. Systems that integrate large language models are no different. They introduce new classes of failure, but they also give us new tools to detect, measure, and correct those failures.
One of the most visible issues in LLM-enabled systems is hallucination. But focusing only on hallucinations misses the bigger picture. The real challenge for engineers is accuracy. More specifically, it is confidence: how do you know your AI application is accurate enough for its intended use?
This article takes a practical, engineering-first approach to that question. It does not assume magical fixes or perfect models. Instead, it treats LLM accuracy as a system property that can be tested, measured, monitored, and improved, just like performance, reliability, or security.
What Hallucination Actually Means
In casual terms, hallucination is when a language model produces output that makes no sense or is plainly wrong. That definition is intuitive, but not very useful for building systems.
A more precise way to think about hallucination is this:
An LLM hallucinates when it generates output that does not align with its training data, the context provided at runtime, or an external source of truth the application depends on.
This framing matters because it moves the problem from psychology-style language into engineering terms. We are not dealing with imagination. We are dealing with misalignment between inputs, constraints, and outputs.
Once framed this way, hallucination becomes one symptom of a broader category: inaccuracy.
Accuracy Is Not Binary
One of the biggest mistakes teams make is treating accuracy as a yes-or-no question. In real systems, accuracy exists on a spectrum. What matters is whether the system is accurate enough for its purpose.
A creative writing assistant and a medical decision support tool have very different accuracy thresholds. The same output that is acceptable in one context would be catastrophic in another. Before you can improve accuracy, you need to define it.
Step One: Understand Your Application and Problem Space
Confidence in accuracy starts with understanding what accuracy means for your application.
This usually comes from two sources:
Guidelines that define acceptable behavior
Data that represents correct or expected outputs
Guidelines might include tone requirements, safety rules, domain constraints, or stylistic standards. Data might include known question-answer pairs, historical system outputs, or curated examples created specifically for testing.
Without this foundation, accuracy becomes subjective and impossible to measure.
Where Accuracy Data Comes From
Teams often assume they must already have perfect labeled data to evaluate accuracy. In practice, there are several viable sources.
1. Existing Examples
If your application replaces or augments an existing system, historical outputs can serve as a reference, provided usage rights and policies allow it.
2. Curated Prompt Suites
You can manually create representative prompts and expected responses. This is slower, but often necessary for critical paths.
3. Synthetic Data
You can generate test cases using an LLM itself. This may sound circular, but it is extremely effective when done carefully.
Engineers have been generating synthetic test data for decades. LLMs simply make it faster and more expressive. The key is not who generated the data, but whether it reflects realistic scenarios.
Treat Accuracy as Testing, Not Philosophy
Once you have data, the next step is deciding when and how to test.
Accuracy evaluation should feel familiar to software engineers. It is just another form of testing.
Instead of unit tests for functions, you are writing tests for AI behavior.
Conceptually, an accuracy pipeline looks like this:
inputs
application under test
outputs
evaluation logic
The challenge is defining the evaluation logic.
Human Evaluation: Useful but Limited
The most straightforward evaluation method is human review. Someone looks at the output and decides whether it is correct. This works, but it does not scale. It is expensive, slow, inconsistent, and difficult to automate.
Human review is best reserved for:
creating ground truth datasets
validating evaluation approaches
auditing high-risk outputs
For continuous testing, you need automation.
Algorithmic Similarity Checks
If you have known good outputs, you can compare generated responses to expected ones using text similarity metrics. This approach is simple and fast,
But it has limitations:
It struggles with paraphrasing
It does not capture semantic correctness well
It ignores tone, reasoning quality, and grounding
Similarity metrics are useful as one signal, not the whole solution.
Using an LLM to Evaluate an LLM
One of the most powerful ideas in modern AI engineering is this: You can use an LLM to evaluate the output of another LLM. This sounds strange at first, but it is extremely practical. The evaluator model does not need to be perfect. It needs to be consistent and aligned with your rubric. The key is the grading rubric.
Designing a Grading Rubric
A grading rubric breaks accuracy into specific, testable dimensions.
For example:
Does the response address the user’s question?
Is the response grounded in the provided context or source of truth?
Is the information factually correct?
Is the tone appropriate?
Does the response follow system instructions and constraints?
Each of these can be evaluated independently.
Instead of asking “Is this answer correct?”, you ask a series of focused questions. Each question becomes a prompt to an evaluator model.
Response-Within-Response Evaluation
Evaluation does not have to be monolithic.
You can:
split responses into sections
evaluate each section separately
aggregate scores into an overall result
This allows you to pinpoint where failures occur. A response might be factually correct but violate tone guidelines. Or it might be polite but ungrounded.
Granularity improves debuggability.
Accuracy Tests as Unit Tests
Once you formalize evaluation prompts, accuracy tests start to look very familiar.
They are inputs, expected properties, and assertions.
You can think of them as unit tests for AI behavior.
Just like traditional tests, they can run:
on every commit
nightly
before release candidates
on demand
The schedule depends on cost and runtime, not on principle.
Comparing Models and Versions
Automated evaluation unlocks another powerful capability: comparison.
You can run the same accuracy tests against:
different models
different versions of the same model
different prompting strategies
different retrieval configurations
This allows you to answer practical questions, such as whether a cheaper model is accurate enough for your use case.
Accuracy becomes a measurable trade-off, not a guess.
Offline Evaluation vs Online Evaluation
So far, we have discussed offline evaluation. This happens outside the user interaction loop.
Offline evaluation is essential, but it does not catch everything.
Real users behave in ways test suites do not anticipate. This is where online evaluation comes in.
Online Evaluation in Production
Online evaluation verifies quality while the system is running in production.
This is similar to assertions or monitoring in traditional software systems.
You identify strategic points in the application where you can check outputs against expectations and take action if something looks wrong.
Output-Level Evaluation
The simplest place to evaluate is at the final output.
Before returning a response to the user, the system checks:
usefulness
accuracy
style
policy compliance
If the response fails, the system can:
retry internally
rephrase the request
ask the user for clarification
return a safe fallback
This approach works, but it has a downside. By the time you catch the error, the entire system has already run.
Evaluation Inside Multi-Agent Systems
Many modern LLM applications use multiple agents coordinated by an orchestrator.
In these systems, errors can propagate. A mistake early in the process contaminates everything downstream.
The solution is early and frequent evaluation.
You can insert evaluation steps:
after each agent
between major reasoning steps
before tool calls
before executing a plan
This prevents bad intermediate outputs from spreading.
Evaluating the Plan Before Execution
One of the most effective evaluation points is the plan itself.
If an orchestrator generates a plan, you can evaluate that plan before executing any steps.
Questions might include:
Does this plan address the user request?
Are there unnecessary steps?
Does it violate constraints?
Is required context missing?
If the plan is flawed, rewrite it before execution. This saves time, cost, and risk.
Trade-Offs in Online Evaluation
Online evaluation is not free.
It adds:
latency
cost
architectural complexity
Not every step needs evaluation. Some steps benefit more from offline testing.
The goal is not maximal evaluation, but strategic evaluation.
Use offline evaluation to design the system. Use online evaluation to protect it.
Catching Errors Early
The biggest advantage of multi-stage evaluation is containment.
If an error is caught at the source, you can:
retry a single agent
adjust inputs
request clarification
You do not need to restart the entire workflow.
This mirrors defensive programming practices in traditional systems.
No Single “Correct” Approach
There is no universal solution for accuracy.
The right approach depends on:
application risk
user expectations
cost constraints
latency requirements
team maturity
What matters is understanding that accuracy is not mysterious. It is something you can engineer.
The Core Insight
The most important takeaway is simple:
You can use AI models to evaluate AI systems.
This makes large-scale accuracy testing practical for the first time. What was once manual and subjective can now be automated, repeatable, and measurable.
Accuracy becomes a system property, not a hope.
Final Thoughts
Hallucination is not a flaw to eliminate. It is a signal that your system lacks sufficient grounding, constraints, or evaluation.
By treating accuracy as an engineering problem, teams can build LLM-enabled applications with confidence.
This requires:
clear definitions
structured test data
automated evaluation
thoughtful placement of checks
acceptance of trade-offs
LLMs are probabilistic systems. They will never be perfect. But with the right architecture, they can be reliable enough to power real applications at scale.
That is the real goal.
