Chatbots today are built in many different ways—some follow rule-based flows, others rely on search and retrieval, and the latest generation uses large language models (LLMs), often in combination with structured APIs. My recent work with chatbots has given me the chance to explore their architectures more deeply and reflect on their different design approaches. In this article, I’ll outline the main categories, highlight their strengths and trade-offs, then focus on GraphQL and RAG as two contrasting examples, before touching on emerging patterns like copilot-style assistants that point toward the next stage of chatbot evolution.
Types of Chatbots by Technical Approach
Over the years, several implementation patterns have emerged. Each represents a different way of balancing precision, flexibility, and usability:
| Type | Key Characteristics | Strengths | Limitations | Typical Use Cases |
|---|---|---|---|---|
| Rule-based | Predefined flows and if–then rules | Simple, predictable, cheap | Inflexible, can’t handle complex or unseen queries | Early FAQ bots, basic customer service |
| Retrieval-based | Searches a fixed knowledge base | Stable, accurate for known questions | Rigid, no creativity, can’t generalize beyond KB | Company FAQs, troubleshooting guides |
| Generative (LLM-based) | Uses large language models to generate answers | Natural, flexible, handles open-ended queries | May hallucinate, less reliable for facts | Conversational assistants, summarization |
| RAG (Retrieval-Augmented Generation) | Retrieval + LLM summarization | Flexible and grounded, reduces hallucination | Needs vector DB & infra, retrieval quality critical | Enterprise knowledge assistants, document Q&A |
| API/Database-driven (GraphQL/SQL) | Translates intent into structured queries | Precise, auditable, access-controlled | Limited to schema, dev effort required | Data governance, BI dashboards |
| Copilot-style | Context-aware, embedded in apps | Integrated, productivity-boosting | Opaque, privacy/security concerns | GitHub Copilot, Microsoft 365 Copilot, Collibra Copilot, Databricks AI/BI Genie |
Each approach has its place. Rule-based and retrieval-based bots are predictable but rigid. Generative bots unlock flexibility but risk hallucination. RAG balances the two by grounding LLMs in documents. API/Database-driven bots prioritize precision and governance, while Copilot-style assistants integrate intelligence directly into everyday tools.
Zooming in: GraphQL-Driven Chatbots
While much of the conversation today centers on LLMs, API-driven bots remain essential in enterprise environments. GraphQL, in particular, offers a powerful way to bridge natural language queries with structured systems.
The flow is simple: a user request is translated into a GraphQL query, executed against a governed backend, and returned as structured results (often JSON). The chatbot then renders these into a readable explanation. This pattern prioritizes precision, reliability, and auditability rather than open-ended creativity.
In contexts like data governance, reporting, and compliance, ambiguity is costly. Users need exact numbers, ownership, or lineage. GraphQL-driven bots shine here by enforcing schema definitions, respecting access controls, and ensuring consistency.
GraphQL vs. RAG: Precision and Flexibility
Although they come from different traditions—GraphQL as an API query language, RAG as an AI architecture—both can underpin chatbots. The difference lies in what they optimize for:
- GraphQL-driven bots thrive when answers live in structured, governed data. They provide authority, auditability, and deterministic results.
- RAG-driven bots excel when knowledge is unstructured. They retrieve passages from documents and let an LLM synthesize a natural-language response, offering flexibility but relying on retrieval quality.
A useful analogy is to think of two librarians:
- The GraphQL librarian checks the catalog and hands you the exact record.
- The RAG librarian skims multiple books and explains the gist.
In practice, the best assistants combine the two: querying APIs for authoritative facts while retrieving unstructured text for context and narrative.
Beyond GraphQL and RAG
Beyond GraphQL and RAG, a growing trend is the rise of copilot-style assistants embedded directly into enterprise tools. Unlike standalone chatbots, these systems don’t just answer questions—they understand context, generate code, and even execute actions within the application itself.
A good example is Databricks AI/BI Genie, which has recently become generally available. Genie allows users to query data in natural language, generate SQL, explore metadata, and derive insights more intuitively. It represents the shift toward integrated copilots: assistants that combine structured queries, retrieval, and generative models within the tools people already use.
This trend points toward a future of hybrid assistants, where precision and flexibility are no longer competing but complementary.
Conclusion
Chatbots can be built in many ways—from rules and retrieval to GraphQL queries and RAG pipelines—each with trade-offs between precision and flexibility. If you’ve worked with different chatbot designs, I’d love to hear your thoughts: what strengths or limitations have you seen in practice?