Designing Conversational AI Systems
Technical approaches, tradeoffs, and decision frameworks to build scalable and reliable AI experiences
This would help product managers choose the right back-end architecture for conversational AI. It explains the three common approaches, shows the tradeoffs that affect cost and quality, and gives clear signals for when to pick each approach.
Framework,
Traditional NLU systems - Use when you need strict control, low per-interaction cost, and predictable behavior for a limited set of tasks.
Standalone LLM chatbots - Use when you want rapid prototyping, wide coverage, or natural, human-like answers and you can tolerate higher cost and occasional inaccuracies.
Hybrid systems - Use when you need a balance: programmatic control for core flows plus generative help for edge cases or complex queries.
Importance of Architecture
Choosing an architecture is one of the highest-impact technical decisions for a conversational product. It is not only a technology choice but a product economics and risk choice.
Here are the core ways your architecture affects outcomes:
Cost at scale
Some approaches charge per model call and can become expensive when usage grows. Others require heavier engineering upfront but are cheaper per interaction.
Example: a simple intent-answer bot can be cheap to run for millions of requests. A general-purpose LLM that generates text every turn will have a much higher per-conversation bill.
Quality and predictability
Architectures that use hand-designed flows give predictable answers and make compliance easier.
Generative models can handle unexpected phrasing and broad topics, but may produce plausible-sounding errors.
Time to market and iteration speed
LLM prototypes are fast to ship because you often only need to write prompts and a little glue code.
NLU systems take time to design and test all dialogs, but once built they are stable and maintainable.
Security, privacy, and compliance
Systems with explicit programmatic control are easier to audit and restrict. They are often required in regulated domains.
LLMs introduce new attack surfaces such as prompt injection and model memorization, so extra guardrails are needed.
User experience and brand fit
If your product needs a friendly, conversational voice that adapts to lots of topics, generative models can add value.
If your product must always be accurate and concise, a rules-based or hybrid approach may be better.
Common misconceptions product teams make
Thinking generative models remove the need for design work. You still must design conversational flows, error states, and handoffs.
Believing that a single approach will be ideal forever. Products evolve and you might prototype with LLMs, then move to a hybrid or more controlled system as scale and requirements change.
Overlooking operational costs. Monitoring, retraining, and data pipelines create ongoing expenses beyond the obvious model bill.
Glossary of core terms
Natural Language Understanding (NLU)
A rules-plus-model approach that classifies user intent and extracts specific data points (entities). Example: identifying that “book a flight to Mumbai next Monday” is a booking intent and extracting “Mumbai” and “next Monday”.Large Language Model (LLM)
A single, large neural model that can read prompts, track short-term context, and generate freeform text. Example: ChatGPT or Claude answering a user question in natural language.Hybrid system
An architecture that uses NLU or rules for structured steps and uses an LLM for freeform or fallback responses. Example: validate identity via rules, then let LLM craft nuanced explanations.Retrieval-Augmented Generation (RAG)
A technique that fetches relevant documents or data and includes them in the prompt to the LLM so answers are grounded in up-to-date facts. Example: retrieving a product manual paragraph to answer a troubleshooting query.Fine-tuning
Training an existing model further on domain-specific examples so it generates replies in a particular style or with specialized knowledge. Example: adapting a model to write in your brand voice.ASR (automated speech recognition)
Converts spoken audio to text so the conversational stack can interpret voice input.TTS (text to speech)
Converts text responses into natural-sounding audio for voice interfaces.Intent
The action the user wants to perform, like “check balance” or “schedule appointment”.Entity
A piece of structured information inside the user’s utterance, such as a date, location, or product name.Dialog state
The record of what has happened in the conversation so far, used to keep context across turns.Prompt
The input given to an LLM that instructs its behavior. It often includes a system-level instruction followed by the user query and any retrieved facts.Hallucination
When a generative model confidently provides incorrect or fabricated information. This is a key safety risk to monitor.
Architectural Patterns
This section breaks down the three core architectures you will choose between.
Traditional NLU systems
A structured, pipeline approach where separate components handle speech-to-text, intent classification, entity extraction, dialog state, business logic, and response rendering. Conversation flows are mostly authored and validated up front.
Key components
ASR (if voice): audio to text.
NLU: intent classifier and entity extractor.
Dialog manager: state tracking, turn logic, slot filling.
Business connectors: APIs to backend systems.
Response renderer: templated replies or simple text generation.
TTS (if voice): text to audio.
Strengths
Predictable, auditable behavior.
Low per-interaction compute cost after build.
Easier to certify for regulated workflows.
Simple failure modes and clear fallbacks.
Weaknesses
High upfront design and maintenance effort.
Brittle with unexpected phrasing or new user flows.
Hard to scale across many topics without lots of authoring.
Example
Bank IVR for balance checks and payments.
Appointment booking where steps are fixed.
High-volume FAQs with stable answers.
