A tutorial can show how to make an AI feature work once; it cannot prove the feature is reliable, safe, or ready for real users. When an application disappoints, resist adding another technique at random. Establish what failed, inspect examples, form a specific hypothesis, change the relevant layer, and evaluate again.
These five dead ends are a practical way to diagnose the gap between a demo and an application. They are an editorial framework, not a formal taxonomy. The core distinction is simple: prompts, retrieval, model training, evaluation, and system permissions address different problems.
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1. Treating prompt, retrieval, and fine-tuning as a recipe ladder
A common tutorial storyline makes each technique sound like the next rung: improve the prompt, add retrieval-augmented generation (RAG), then fine-tune. That sequence can waste time because the methods solve different failure classes. OpenAI advises an iterative approach: set a baseline, inspect incorrect examples, choose an intervention based on a hypothesis, and evaluate again. Its guide also notes, “Optimizing LLMs is hard.” (OpenAI’s guide to optimizing LLM accuracy.)
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- Missing, stale, or proprietary knowledge: Improve the context available to the model, for example by retrieving appropriate source material.
- Inconsistent format, tone, or task behavior: Improve instructions or consider model-behavior optimization where appropriate.
- A task-specific behavior that needs to be learned or made more efficient: Fine-tuning may be relevant, but it is not a general-purpose “make it smarter” button.
Before choosing a method, write down the observed failure and the change you expect to fix it. If the answer is wrong because the necessary information was never provided, changing style instructions is unlikely to solve the underlying problem. If the context is correct but the output format varies, adding more documents may not help.
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For fine-tuning, use examples that represent real inputs and keep a separate hold-out set for evaluation. A model that improves on its training examples but fails on unseen cases may have overfit; do not use the same examples to train and to claim success.
2. Expecting RAG to switch hallucinations off
RAG makes external or domain-specific information available to a model. It does not guarantee that the retrieved material is relevant, complete, concise, or used correctly. A system can fail before generation because retrieval returns the wrong material—or too much noisy context. It can also fail after retrieval when the model ignores or misinterprets otherwise useful evidence.
Separate retrieval failures from answer failures
- Check what was retrieved. For a representative failed query, inspect the actual passages supplied to the model. Ask whether they contain the needed evidence and whether irrelevant material is crowding it out.
- Check what the model did with it. If the context is suitable, inspect the instructions and answer to see whether the model followed the evidence, misunderstood it, or made an unsupported inference.
- Change one layer at a time and re-evaluate. Tune retrieval when the evidence is missing or noisy; tune instructions or model behavior when good evidence is available but used badly.
This split makes “why does RAG still hallucinate?” a diagnosable question rather than a reason to add a vector database and assume the problem is solved. Retrieval quality and generation quality both need assessment. (OpenAI’s accuracy-optimization guidance.)
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3. Calling a working demo “evaluated”
A successful run proves only that one input produced an acceptable result. It does not show how the application behaves across the inputs, edge cases, and mistakes that matter to its users. Start evaluation before deciding a feature is ready.
Build a useful baseline
- Collect examples that represent the intended task and likely real inputs, including cases that previously failed.
- Define what counts as an acceptable result for the application—not just what looks plausible in a demo.
- Measure the current system before changing it, so you can tell whether an intervention helped.
- Assess the cost of errors in context. A wrong answer in a low-stakes draft is not equivalent to a wrong action in a consequential workflow.
There is no universal accuracy percentage or safety threshold that establishes readiness for every application. The acceptable level depends on the task, users, and consequences of errors. OpenAI’s optimization guidance recommends representative evaluation and inspecting incorrect examples; its safety guidance recommends red-teaming with representative and deliberately adversarial inputs, plus human review where practical—especially for high-stakes domains and code generation. (OpenAI’s safety best practices.)
Test the safety risks your application actually has
Google recommends defining context-specific safety metrics and minimum acceptable levels before testing. Off-the-shelf datasets may not cover an application’s particular risks, so add tests for its likely users and failure modes. Probe rare and unusual cases as well as ordinary ones; outputs can vary between runs, so repeat tests when that variability matters. (Google’s safety evaluation guidance.)
If training or fine-tuning, reserve hold-out examples to check performance on data the model did not see during training. For outputs that people must act on, make source material available to reviewers where feasible so they can verify summaries and other generated claims rather than relying on fluent wording alone. (OpenAI’s safety best practices.)
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An application that reads webpages, uploaded files, retrieved documents, or tool results may encounter text written to redirect the model. This is prompt injection: content that should be treated as data can instead be interpreted as an instruction. The risk becomes more serious when a model can call tools, because following hostile text may trigger unintended actions or expose private information through later calls. OpenAI describes these risks in its agent safety guidance and safety best practices.
Reduce the consequences of an injection
- Use trusted or audited connectors for external content.
- Constrain tool arguments with schemas or application-side validation rather than accepting arbitrary model-generated values.
- Log and review tool calls so unexpected behavior can be investigated.
- Where appropriate, stage workflows so public-web research and access to private data are separated instead of being available together in one unrestricted flow.
These controls reduce exposure; they do not establish that every attack can be automatically detected. Treat externally supplied text as untrusted, and design the surrounding workflow so a mistaken instruction cannot automatically reach sensitive data or actions.
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5. Treating a prompt as an access-control system
A prompt can express a desired rule, but it cannot enforce operating-system, database, or application permissions. The US Department of Energy’s GEAR guidance puts it plainly: “A prompt such as ‘never delete files’ is not an access control.” (US Department of Energy GEAR, AI Security and Safety.)
Start with the lowest capability level that can do the job, following the principle of least privilege. Enforce authorization in the application or in the relevant operating system, scheduler, database, gateway, or policy layer—not only in model instructions. Give the system only the data, tools, and autonomy it needs.
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Put operational limits and recovery outside the prompt
- Set limits on time, cost, tokens, retries, jobs, and actions.
- Provide a way to cancel work and a recovery or rollback path for changes.
- Require human approval before consequential operations, such as writing shared data, deleting information, installing software, sending external communications, or using output for consequential decisions.
- Where a person reviews a generated summary or other output, provide the underlying source material when practical so the reviewer can verify it.
These safeguards make authority enforceable and failures easier to contain. A request in natural language is not a substitute for permissions, limits, approval gates, or recovery controls. The Department of Energy’s GEAR security and safety guidance discusses least privilege and capability levels.
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How to choose the next fix
Use the observed failure—not the popularity of a technique—as your starting point. For each proposed change, consider whether it addresses the diagnosed cause, what data or access it requires, its implementation and operating cost, how reversible it is, and what happens if it fails. Evaluation and context-specific risk analysis can guide the choice; there is no universal numerical trade-off that applies to every application.
| Observed failure | First layer to inspect | Useful next move |
|---|---|---|
| The answer lacks current, proprietary, or domain-specific facts | Available context and its freshness | Provide or retrieve appropriate source material, then test whether the model uses it correctly. |
| Retrieved passages are irrelevant, excessive, or miss needed evidence | Retrieval | Inspect retrieved results for representative failures and adjust retrieval before changing model behavior. |
| Good evidence is present, but format, tone, or task behavior is inconsistent | Instructions and model behavior | Test an instruction change; consider task-specific model optimization only if evaluation supports it. |
| External content can redirect tool use or reach private data | Input trust boundaries and tool workflow | Constrain and validate calls, review them, and separate public input from sensitive access where appropriate. |
| The system can take actions beyond what the task needs | Permissions and operational controls | Reduce privileges, add limits and approval gates, and provide cancellation or rollback. |
The diagnostic loop is the part tutorials often leave out: establish a representative baseline, inspect the failure, identify the layer responsible, make a targeted change, and evaluate again. A demo is a starting point, not evidence of reliable behavior.
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