To build a useful chatbot or AI assistant, start with the user’s task and choose the simplest design that can handle it: a direct model call for straightforward answers, a fixed workflow for predictable steps, or an agent when the system must decide what to do next and use tools. Add retrieval when answers need to rely on a defined set of documents, then evaluate the complete experience—including retrieval, safety, and handoffs—before deployment.
What is the difference between a chatbot and an AI agent?
A chatbot is a conversational interface; that label alone does not say how much control the system has. It may return a single model-generated answer without taking further action. An agent manages a workflow: it can decide what step to take, select a tool, use the result, and continue under instructions and safeguards.
OpenAI’s agent guidance draws the distinction around workflow control: an application that uses an LLM but does not let it control workflow execution, such as a simple chatbot or a single-turn LLM call, is not an agent. The distinction matters because agent behavior adds decision-making and integration work that a simple answer interface may not need.
How do you choose an architecture?
Write down how predictable the task is, what the system may do, what happens when a step fails, and how much autonomy is actually necessary. Prefer deterministic code when the steps are known. Consider agent control when the system needs to choose among tools or handle variable, multi-step work. OpenAI and Anthropic both advise checking that agent behavior benefits the task rather than adding orchestration by default.
#1 Best Overall
| Approach | Good fit | Main trade-offs to assess |
|---|---|---|
| Direct model call | A narrow conversational task or a single-turn response. | Control and integration needs, answer quality, latency, and how the interface handles errors or uncertainty. |
| Fixed workflow | A task with known stages, such as a sequence of model calls and programmatic checks. | Predictability and easier control versus the effort of defining and maintaining each branch. |
| Agent | A task where the system must choose what to do next, select tools, or adapt a multi-step workflow. | Autonomy and flexibility versus more complex failure recovery, evaluation, security, and implementation. |
Anthropic describes prompt chaining as one way to divide a task into steps and insert programmatic checks. Routing can send different input classes to distinct prompts or handlers. These patterns can cover many cases without giving a model broad control over execution.
How do you build a chatbot with your own data?
When answers need to draw on private or domain-specific material, retrieval-augmented generation (RAG) can find relevant passages and place them in the model’s context. The model can then answer using those passages instead of relying only on what it learned during training. RAG does not by itself guarantee a correct answer: retrieval may miss relevant material, and the model may still misinterpret what it receives.
Prepare and index the material
- Choose representative documents and questions. Include the kinds of material users will ask about and queries that test whether relevant passages can be found. This gives you a basis for comparing retrieval choices.
- Break content into meaningful chunks. Choose boundaries that preserve enough context for a passage to make sense. Chunking affects what the search system can retrieve, so assess it against the representative questions rather than selecting it in isolation.
- Add useful metadata where appropriate. Attributes such as document type or access category can help organize and filter results. Metadata should reflect the actual content and permissions the application needs to enforce.
- Embed and index the chunks. An embedding represents content for semantic search; the index makes it possible to retrieve relevant material for a query. Embeddings, chunking, and search configuration can all change retrieval relevance.
Retrieve evidence at answer time
In standard RAG, the application accepts a query, searches the index, supplies the query and selected results to the model, and returns the response. Microsoft’s Azure AI guidance describes this fixed sequence as suited to single-search cases. Where users need to inspect the basis of an answer, show relevant sources or otherwise make its grounding visible.
Rank #2
Consider agentic RAG when a task needs multiple or variable retrieval steps, query decomposition, dynamic source selection, or retrieval combined with actions. In this design, retrieval is a tool the agent can invoke. It can accommodate more complex work, but adds orchestration and increases the need to test each step as well as the final response.
NIST NCCoE’s internal chatbot for finding and summarizing cybersecurity guidance from its publications is an example of RAG in practice. Its IR 8579 draft discusses risks including prompt injection, hallucinations, data exposure, and unauthorized access, and describes safeguards such as local deployment, access controls, and validation filters. The report documents a point-in-time prototype; NIST explicitly says it is not implementation guidance, so its safeguards should not be treated as a universal recipe.
When should you add tools or actions?
A tool gives the model a way to retrieve information or request an operation through the surrounding application. Keep each tool narrow and explicit so its purpose and limits are clear.
- Document its purpose, inputs, outputs, and expected errors.
- Limit access to the systems and operations the task requires.
- Separate read-only retrieval from actions that change records or affect people.
- For consequential actions, define authorization and any human approval or confirmation the workflow requires.
- Plan how tool calls, failures, and results will be observed and debugged.
Google Cloud’s architecture guidance notes that custom function descriptions tell the model when and how to use a tool, and calls out observability, debugging, and error handling as design concerns. In enterprise systems, API governance and data permissions must be part of the tool design, not left to the model’s instructions.
How should you test the complete system?
Test more than whether the model can produce a fluent answer. Build a stable set of representative queries and documents, define acceptable outcomes for the task, and compare changes against the same targets. Include ordinary requests as well as cases that expose failure modes.
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- Retrieval: Does the search return the passages needed to answer the query? Check whether relevant material appears among the results and whether irrelevant material crowds it out.
- Grounding: Does the response stay supported by the retrieved material, and can a reader inspect its sources when that is important?
- Completeness and relevance: Does it address the user’s request without omitting essential information or drifting into unrelated detail?
- Workflow behavior: Does the system choose the right route or tool, handle errors, and hand off or decline when required?
- Safety and fairness: Does it respect the application’s behavior boundaries across representative inputs?
Microsoft lists groundedness, completeness, utilization, and relevance as possible end-to-end RAG evaluation dimensions. The exact measures and pass criteria should follow the intended use; no single score establishes that a system is ready for deployment.
Rank #4
Include difficult and out-of-scope cases
Test questions whose answers are absent from the knowledge base, ambiguous requests, adversarial instructions, and situations requiring refusal or human handoff. For retrieval systems, check whether the application handles weak or missing evidence appropriately. For tool-using systems, test unauthorized requests and tool errors as well as successful calls.
Establish a performance baseline before changing prompts or models. OpenAI recommends then checking whether a faster or less costly model still meets the required accuracy. Make that decision using the same evaluation set and task criteria; a model or design should not be called better without comparative evidence.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do you handle safety, privacy, and security?
Set the assistant’s allowed and disallowed behavior before connecting it to sensitive data or actions. Define when it should answer, when it should decline, and when it should hand control to a person. Choose safeguards for the specific use case, then test them as part of the complete system.
- Prompt injection: Treat user content and retrieved documents as potentially untrusted instructions; check that they cannot override the application’s intended behavior or permissions.
- Hallucination: Test unsupported questions and weak retrieval results, and make the intended response to missing evidence explicit.
- Data exposure: Restrict which documents and records each user and tool can access, and ensure logging follows the application’s data-handling requirements.
- Unauthorized actions: Enforce permissions in the application and connected services rather than relying solely on a model instruction.
- Fairness and factuality: Evaluate relevant safety, fairness, and factuality concerns using cases appropriate to the people and decisions affected.
Safeguards should be proportionate to the consequences of an error. An assistant that only summarizes public information has different exposure than one that can read private records or change them.
How do you deploy and maintain an assistant?
Select the model access, runtime, frontend, storage, retrieval service, and tools around the workload and operating requirements. Compare options on security, data access, observability, scalability, operational burden, latency, cost, and implementation effort; the reviewed architecture guidance does not establish a universal deployment choice or current prices.
Log enough information to diagnose retrieval, model, and tool failures while respecting privacy and data-retention requirements. Reassess behavior, retrieval quality, and safeguards when the underlying documents, workload, integrations, or model change. Keep the same evaluation targets available so that changes can be checked rather than assumed to improve the system.
What is a sensible first version?
- Define the users, task, boundaries, and handoff conditions.
- Build the simplest baseline that fits: direct model call or a fixed workflow.
- Add RAG only if the answers need a defined body of information, and evaluate retrieval with representative documents and questions.
- Add narrowly scoped tools only for necessary retrieval or actions, with permissions enforced outside the model.
- Test ordinary, ambiguous, out-of-scope, adversarial, and failure cases against explicit targets.
- Deploy with appropriate logging and access controls, then re-evaluate as the system changes.
Anthropic’s 2024 article Building Effective AI Agents advises developers to start with direct LLM API use where practical and understand the framework beneath any abstraction. That is a useful starting principle, not a reason to reject frameworks: use one when its capabilities solve a real integration or orchestration need and its behavior remains understandable.
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