ai-building-chatbots

name

ai-building-chatbots

description

Build a conversational AI assistant with memory and state. Use when you need a customer support chatbot, helpdesk bot, onboarding assistant, sales qualification bot, FAQ assistant, or any multi-turn conversational AI. Also used for chatbot remember previous messages, conversational AI keeps forgetting context, build a helpdesk bot that actually works, chatbot drops context after a few turns, Intercom bot alternative, Zendesk AI alternative, build WhatsApp bot, Slack bot with AI, chatbot escalation to human agent, LangChain chatbot but simpler, chatbot for SaaS onboarding flow.

Build a Conversational AI Chatbot

Guide the user through building a multi-turn chatbot that remembers context, follows conversation flows, and produces high-quality responses. Uses DSPy for optimizable response generation and LangGraph for conversation state, memory, and flow control.

When not to build a chatbot

Skip multi-turn chatbot architecture when a single-turn LM call is sufficient. A user who submits a support ticket and gets a one-shot email reply does not need conversation state — use /ai-writing-content. A search box that returns a direct answer does not need LangGraph — use /ai-searching-docs. Chatbot infrastructure adds complexity that only pays off when the task genuinely requires multiple back-and-forth exchanges.

Signal	Better approach
User submits once, expects one response	/ai-writing-content or single dspy.Module
User searches a knowledge base	/ai-searching-docs (RAG without state)
Bot takes actions autonomously	/ai-taking-actions (tool use, not chat)
Multiple AI agents coordinating	/ai-coordinating-agents
Conversation is always 1-2 turns	Plain dspy.ChainOfThought, no LangGraph

Step 1: Define the conversation

Ask the user:

1. What does the bot do? (answer questions, resolve issues, qualify leads, guide onboarding?)
2. What states can the conversation be in? (greeting, gathering info, resolving, escalating, closing?)
3. When should the bot escalate to a human? (complex issues, angry users, sensitive topics?)
4. What docs or data should it draw from? (help articles, product docs, FAQs, database?)

Step 2: Build the response module (DSPy)

The core of your chatbot is a DSPy module that generates responses given conversation history and context.

lm = dspy.LM("openai/gpt-4o-mini")  # or "anthropic/claude-sonnet-4-5-20250929", etc.
dspy.configure(lm=lm)

Basic response module

import dspy

class ChatResponse(dspy.Signature):
    """Generate a helpful, on-brand response to the user's message."""
    conversation_history: str = dspy.InputField(desc="Previous messages in the conversation")
    context: str = dspy.InputField(desc="Relevant information from docs or database")
    user_message: str = dspy.InputField(desc="The user's latest message")
    response: str = dspy.OutputField(desc="Helpful response to the user")

class ChatBot(dspy.Module):
    def __init__(self):
        self.respond = dspy.ChainOfThought(ChatResponse)

    def forward(self, conversation_history, context, user_message):
        return self.respond(
            conversation_history=conversation_history,
            context=context,
            user_message=user_message,
        )

With intent classification

Route different intents to specialized handlers:

from typing import Literal

class ClassifyIntent(dspy.Signature):
    """Classify the user's intent from their message."""
    conversation_history: str = dspy.InputField()
    user_message: str = dspy.InputField()
    intent: Literal["question", "complaint", "request", "greeting", "goodbye"] = dspy.OutputField()

class ChatBotWithRouting(dspy.Module):
    def __init__(self):
        self.classify = dspy.Predict(ClassifyIntent)
        self.respond_question = dspy.ChainOfThought(AnswerQuestion)
        self.respond_complaint = dspy.ChainOfThought(HandleComplaint)
        self.respond_request = dspy.ChainOfThought(HandleRequest)
        self.respond_greeting = dspy.Predict(Greeting)

    def forward(self, conversation_history, context, user_message):
        intent = self.classify(
            conversation_history=conversation_history,
            user_message=user_message,
        ).intent

        handler = {
            "question": self.respond_question,
            "complaint": self.respond_complaint,
            "request": self.respond_request,
            "greeting": self.respond_greeting,
        }.get(intent, self.respond_question)

        return handler(
            conversation_history=conversation_history,
            context=context,
            user_message=user_message,
        )

Step 3: Add conversation state (LangGraph)

LangGraph manages the conversation flow — what state the bot is in, when to transition, and when to escalate.

Define conversation state

from langgraph.graph import StateGraph, START, END
from typing import TypedDict, Annotated
import operator

class ConversationState(TypedDict):
    messages: Annotated[list[dict], operator.add]  # full message history
    current_intent: str
    context: str           # retrieved docs/data for current turn
    escalate: bool         # whether to hand off to a human
    resolved: bool         # whether the issue is resolved
    turn_count: int

Build the conversation graph

import dspy

# Initialize DSPy modules
classifier = dspy.Predict(ClassifyIntent)
responder = dspy.ChainOfThought(ChatResponse)

def classify_node(state: ConversationState) -> dict:
    """Classify the user's intent."""
    history = format_history(state["messages"][:-1])
    user_msg = state["messages"][-1]["content"]
    result = classifier(conversation_history=history, user_message=user_msg)
    return {"current_intent": result.intent}

def retrieve_node(state: ConversationState) -> dict:
    """Retrieve relevant docs for the current message."""
    user_msg = state["messages"][-1]["content"]
    # Your retrieval logic here (see /ai-searching-docs)
    docs = retrieve_relevant_docs(user_msg)
    return {"context": "\n".join(docs)}

def respond_node(state: ConversationState) -> dict:
    """Generate a response using DSPy."""
    history = format_history(state["messages"][:-1])
    user_msg = state["messages"][-1]["content"]
    result = responder(
        conversation_history=history,
        context=state["context"],
        user_message=user_msg,
    )
    return {
        "messages": [{"role": "assistant", "content": result.response}],
        : state[] + ,
    }

 () -> :
    
    should_escalate = (
        state[] == 
         state[] > 
    )
     {: should_escalate}

 () -> :
     .join(  m  messages[-:])


graph = StateGraph(ConversationState)
graph.add_node(, classify_node)
graph.add_node(, retrieve_node)
graph.add_node(, respond_node)
graph.add_node(, check_escalation)

graph.add_edge(START, )
graph.add_edge(, )
graph.add_edge(, )
graph.add_edge(, )

 () -> :
     state[]:
         
     

graph.add_conditional_edges(
    ,
    route_after_escalation_check,
    {: END, : END},
)

app = graph.()

Run a conversation turn

result = app.invoke({
    "messages": [{"role": "user", "content": "How do I reset my password?"}],
    "current_intent": "",
    "context": "",
    "escalate": False,
    "resolved": False,
    "turn_count": 0,
})
print(result["messages"][-1]["content"])

Step 4: Add memory

Session memory with checkpointing

LangGraph's checkpointer persists conversation state across requests:

from langgraph.checkpoint.memory import MemorySaver

checkpointer = MemorySaver()
app = graph.compile(checkpointer=checkpointer)

# Each user session gets a unique thread_id
config = {"configurable": {"thread_id": "user-abc-123"}}

# Turn 1
result = app.invoke(
    {"messages": [{"role": "user", "content": "Hi, I need help with billing"}],
     "current_intent": "", "context": "", "escalate": False, "resolved": False, "turn_count": 0},
    config=config,
)

# Turn 2 — state is preserved, the bot remembers the conversation
result = app.invoke(
    {"messages": [{"role": "user", "content": "I was charged twice last month"}]},
    config=config,
)

For production, use a persistent backend:

from langgraph.checkpoint.postgres import PostgresSaver

checkpointer = PostgresSaver(conn_string="postgresql://user:pass@localhost/chatbot")
app = graph.compile(checkpointer=checkpointer)

Conversation summary for long chats

When conversations get long, summarize older messages to stay within token limits:

class SummarizeConversation(dspy.Signature):
    """Summarize the conversation so far, preserving key details."""
    conversation: str = dspy.InputField()
    summary: str = dspy.OutputField(desc="Concise summary of the conversation so far")

summarizer = dspy.Predict(SummarizeConversation)

def maybe_summarize(state: ConversationState) -> dict:
    """Summarize if conversation is getting long."""
    if len(state["messages"]) > 20:
        history = format_history(state["messages"][:-5])
        summary = summarizer(conversation=history).summary
        # Keep summary + last 5 messages
        return {
            "messages": [
                {"role": "system", "content": f"Summary of earlier conversation: {summary}"},
                *state["messages"][-5:],
            ]
        }
    return {}

Step 5: Ground responses in docs

Retrieve relevant documents each turn to keep responses factual.

class DocGroundedResponse(dspy.Signature):
    """Answer the user's question based on the provided documentation.
    Only use information from the docs. If the docs don't cover it, say so."""
    conversation_history: str = dspy.InputField()
    docs: list[str] = dspy.InputField(desc="Relevant documentation passages")
    user_message: str = dspy.InputField()
    response: str = dspy.OutputField()

class GroundedChatBot(dspy.Module):
    def __init__(self, retriever):
        self.retriever = retriever
        self.respond = dspy.ChainOfThought(DocGroundedResponse)

    def forward(self, conversation_history, user_message):
        # Retrieve docs relevant to the current message
        docs = self.retriever(user_message).passages
        return self.respond(
            conversation_history=conversation_history,
            docs=docs,
            user_message=user_message,
        )

See /ai-searching-docs for setting up retrievers and vector stores, including loading data from PDFs, Notion, and other sources with LangChain document loaders.

Step 6: Add guardrails

Response quality with dspy.Refine

Use dspy.Refine with a reward function to enforce guardrails on chatbot responses:

class GroundedChatBotInner(dspy.Module):
    def __init__(self, retriever):
        self.retriever = retriever
        self.respond = dspy.ChainOfThought(DocGroundedResponse)

    def forward(self, conversation_history, user_message):
        docs = self.retriever(user_message).passages
        return self.respond(
            conversation_history=conversation_history,
            docs=docs,
            user_message=user_message,
        )


def chatbot_response_reward(args, pred):
    """Score chatbot response quality. Returns 0.0-1.0."""
    response = pred.response
    score = 1.0

    # Hard constraint -- don't break character
    if "I am an AI" in response:
        return 0.0

    # Soft penalties
    if len(response.split()) >= 200:
        score -= 0.2  # prefer concise responses

    condescending = ["obviously", "clearly", "simply"]
    if any(word in response.lower() for word in condescending):
        score -= 0.1  # avoid condescending language

    return max(score, 0.0)


def ():
     dspy.Refine(
        module=GroundedChatBotInner(retriever),
        N=,
        reward_fn=chatbot_response_reward,
        threshold=,
    )

Human-in-the-loop for sensitive actions

Use LangGraph's interrupt to pause before the bot takes real actions:

app = graph.compile(
    checkpointer=checkpointer,
    interrupt_before=["execute_refund", "cancel_account"],  # pause here
)

# Bot runs until it reaches a sensitive action
result = app.invoke(input_state, config)

# Human agent reviews the proposed action
# If approved, resume:
result = app.invoke(None, config)  # continues from checkpoint

Step 7: Optimize and evaluate

Conversation-level metrics

def chatbot_metric(example, prediction, trace=None):
    """Score a single conversation turn."""
    judge = dspy.Predict(JudgeTurn)
    result = judge(
        user_message=example.user_message,
        expected_response=example.response,
        actual_response=prediction.response,
        conversation_history=example.conversation_history,
    )
    return result.is_good

class JudgeTurn(dspy.Signature):
    """Judge if the chatbot response is helpful, accurate, and on-topic."""
    user_message: str = dspy.InputField()
    expected_response: str = dspy.InputField()
    actual_response: str = dspy.InputField()
    conversation_history: str = dspy.InputField()
    is_good: bool = dspy.OutputField()

Build a training set from real conversations

trainset = []
for convo in real_conversations:
    for turn in convo["turns"]:
        trainset.append(
            dspy.Example(
                conversation_history=turn["history"],
                user_message=turn["user_message"],
                context=turn["context"],
                response=turn["response"],
            ).with_inputs("conversation_history", "user_message", "context")
        )

Optimize

optimizer = dspy.MIPROv2(metric=chatbot_metric, auto="medium")
optimized_bot = optimizer.compile(chatbot, trainset=trainset)

# Save optimized prompts
optimized_bot.save("chatbot_optimized.json")

Typical gains from optimization: zero-shot baseline ~55-65% turn-level quality score; after BootstrapFewShot with 3-5 demos, ~70-80%; MIPROv2 on 50+ examples typically reaches 80-88%. Run dspy.Evaluate on a held-out devset before and after to confirm improvement.

Verify

devset = [...]  # held-out conversation turns
evaluator = dspy.Evaluate(devset=devset, metric=chatbot_metric, num_threads=4, display_progress=True)

# Baseline
baseline_score = evaluator(chatbot)
print(f"Baseline: {baseline_score:.1%}")

# After optimization
optimized_score = evaluator(optimized_bot)
print(f"Optimized: {optimized_score:.1%}")

Also spot-check that result.response does not start with "I am an AI", does not exceed 200 words, and does not repeat the user's question verbatim.

Key patterns

• DSPy for response generation, LangGraph for flow control — DSPy modules handle what the bot says; LangGraph handles conversation state and routing
• Checkpointing is your memory — use LangGraph's checkpointer so conversations persist across HTTP requests
• Retrieve every turn — don't assume context from earlier turns is still relevant; re-retrieve each time
• Summarize long conversations — once past ~20 messages, summarize older context to stay within token limits
• Classify intent early — knowing the user's intent lets you route to specialized handlers
• Interrupt before real actions — use LangGraph's interrupt_before so humans approve refunds, cancellations, etc.
• Optimize on real conversations — collect actual chat logs to build training data for DSPy optimization

Gotchas

• Claude puts conversation flow logic inside DSPy modules. DSPy modules should only handle LM calls (classify, respond, summarize). State transitions, routing, and memory belong in LangGraph nodes and edges. If you catch yourself writing if/else chains inside forward() to manage conversation state, move that logic to LangGraph.
• Claude passes full message history every turn. This works for short conversations but blows up token usage on long ones. After ~20 messages, summarize older messages and keep only the summary + last 5 messages. Use the maybe_summarize pattern from Step 4.
• Claude forgets with_inputs() when building conversation training data. Every dspy.Example for chatbot training needs .with_inputs("conversation_history", "user_message", "context") — without it, the optimizer treats all fields as outputs and optimization silently produces garbage.
• Claude defines a single monolithic ChatResponse signature for all intents. Different intents need different handling — a complaint needs empathy and escalation logic, a question needs retrieval accuracy, a greeting needs brevity. Use ClassifyIntent + separate handler modules per intent rather than one signature trying to do everything.
• Claude skips the escalation check. Chatbots that can't hand off to humans are a liability. Always include an escalation path — at minimum, a turn-count threshold combined with intent detection for complaints or sensitive topics.

Additional resources

• For worked examples (support bot, FAQ assistant), see examples.md
• For API reference (dspy.History, dspy.Refine, LangGraph checkpointers), see reference.md

Cross-references

Install any skill: npx skills add lebsral/DSPy-Programming-not-prompting-LMs-skills --skill <name>

• Search docs for grounding — see /ai-searching-docs
• Bot takes actions (APIs, tools) — see /ai-taking-actions
• Multiple bots working together — see /ai-coordinating-agents
• Measure and improve chatbot accuracy — see /ai-improving-accuracy
• Multi-step AI pipeline design — see /ai-building-pipelines
• Composing DSPy modules — see /dspy-modules
• Iterative refinement with reward functions — see /dspy-refine
• ReAct for tool-using chatbots — see /dspy-react
• Install /ai-do if you do not have it — it routes any AI problem to the right skill and is the fastest way to work: npx skills add lebsral/DSPy-Programming-not-prompting-LMs-skills --skill ai-do