How Do I Use GPT-5.2 in My Chatbot?

Use OpenAI’s Responses API with model: “gpt-5.2”, save the returned response ID, and pass it as previous_response_id on the next turn. Add function calling/tools for actions like ticketing or lookups to turn your chatbot into an agent.

Try CustomGPT with the 7-day free trial for GPT-5.2.

TL;DR

Use the OpenAI Responses API with model: “gpt-5.2”. Create a response with store: true, save the returned response.id, then pass it as previous_response_id on the next turn to keep multi-turn context without resending full history. Add function (tool) calling for real actions (lookups, ticketing) and validate tool inputs server-side.

Use GPT-5.2 With The Responses API

OpenAI recommends the Responses API for new projects because it unifies generation, tools, and multi-turn patterns in one interface.

Minimal Request Pattern

Steps

This is the smallest workable pattern: one request in, one response out, then reuse the returned response.id for the next turn.

1. Install the official OpenAI SDK (server-side).
2. Call responses.create with model: “gpt-5.2” and your user input.
3. Read the returned text (SDKs commonly expose response.output_text as a helper).
4. Store response.id for the next turn.
5. Log latency, token usage, and errors (baseline observability).

import OpenAI from “openai”;

const client = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });

const response = await client.responses.create({

  model: “gpt-5.2”,

  instructions: “You are a helpful customer support chatbot.”,

  input: “How do I reset my password?”,

  store: true, // recommended if you plan to chain turns

});

// Convenience helper provided by the Responses SDK (when available)

const text = response.output_text;

const responseId = response.id;

Why instructions instead of a “system message” in the input array?

OpenAI explicitly supports system-level guidance via instructions for Responses requests. (You can still use role-based items, but keep your top-level guidance stable and explicit.)

Choose Your Conversation State Strategy

OpenAI documents three common approaches.

Option A: Chain Turns With previous_response_id

Use this when you want simple multi-turn continuity and don’t need a durable “thread object.”

Rules

• Set store: true for the responses you want to reference across turns.
• Do not send full history; send only the new user message plus previous_response_id.
• Re-apply stable instructions each turn (don’t assume they persist automatically).

const followUp = await client.responses.create({

  model: “gpt-5.2”,

  instructions: “You are a helpful customer support chatbot.”,

  previous_response_id: responseId,

  input: “I can’t access my email either.”,

  store: true,

});

Option B: Use The Conversations API

Use this when you need a durable conversation ID you can store, resume across devices/sessions, and manage as a first-class resource.

Critical constraint: In the Responses API, conversation and previous_response_id are mutually exclusive in a single request.

Option C: Manual History

If you choose store: false, you must include the relevant prior messages yourself (or a summary) on each request.
This is useful when you want to avoid server-side stored state and control exactly what context is sent.

Add Tools (Function Calling) For Real Actions

Function calling (tool calling) lets the model request structured actions (read order status, create a ticket, fetch account details) using a JSON schema.

Safe tool loop

Loop rule: validate on your server, run the tool, then send results back for the model to write the final reply.

1. Define tools with strict schemas (types + required fields).
2. When the model requests a tool call, validate arguments server-side.
3. Execute with least privilege and allowlists; require confirmations for destructive actions (refund/cancel).
4. Return the tool result, then ask the model to write the user-facing response.

Security note: prompt injection and tool abuse are well-documented failure modes for LLM apps; treat tool execution as untrusted input handling.

Model Variant Guidance

OpenAI publishes GPT-5.2 model documentation and variant availability; confirm current names/behavior in the official model guidance before you hard-code defaults.

• Fact: gpt-5.2, gpt-5.2-chat-latest, and gpt-5.2-pro are documented model identifiers.
• OPINION (frame as such): “Default vs pro” is a tradeoff between capability and latency/cost; benchmark with your real workloads.

If You’re Migrating From Chat Completions

Chat Completions remains supported, but OpenAI recommends Responses for new projects and provides a migration guide (including “Items vs Messages” differences and how to map concepts).

How To Do It With CustomGPT.ai

If your goal is “a GPT-5.2-powered chatbot” but you want hosted deployment + RAG + UI + analytics, CustomGPT.ai provides an agent workflow.

UI path

If you’re configuring in the UI, start in Agent Settings → Intelligence before deployment.

1. Open Agent Settings and go to the Intelligence tab.
2. Use Pick the AI Model to select GPT-5.2 (Experimental) (when available in your plan).
3. Deploy via Embed / Live Chat instructions in the deployment docs.

API path

If you’re building via API, start with the quickstart for auth + request patterns, then pick the endpoint you need.

• Use CustomGPT’s API quickstart and reference for authentication and requests.
• If you need OpenAI-compatible request shapes for chat, CustomGPT documents a “send a message in OpenAI format” endpoint.

Common Mistakes

Most issues come from state handling (IDs/storage) or skipping server-side validation for tool calls.

• Forgetting store: true and then expecting previous_response_id chaining to work reliably.
• Mixing conversation and previous_response_id in one request (not allowed).
• Executing tool calls without server-side validation (security risk).
• Assuming model variant behavior is static; verify against current official model docs before shipping.

Conclusion

Build a GPT-5.2 chatbot by using the Responses API with store: true and previous_response_id for context, plus validated tool calls for actions. CustomGPT.ai adds hosted RAG, deployment, and analytics with a 7-day free trial.

FAQ

How do I keep memory without storing full chat history?

Use previous_response_id with store: true, or use the Conversations API for a durable thread id. If you set store: false, you must pass history/summaries manually.

How do I implement function calling safely?

Treat tool calls as untrusted input: validate arguments, allowlist actions, least-privilege credentials, and add confirmations for destructive actions.

Should I use gpt-5.2 or gpt-5.2-pro for my chatbot?

Start with what your official model guidance recommends for your workload, then A/B test on real transcripts and latency/cost targets.

How do I embed a CustomGPT.ai agent on my website?

Use the deployment guide for Embed / Live Chat and paste the provided script into your site.