Hi! I just want to ask if there’s any way that I can integrate Chatgpt in my website but I don’t want like a chatbot. I want more of a chat feature front and centre in the website. It’ll be specialised in giving advice on a certain topic. And another question, I’m also finding a way that I can generate images based on text inputs (I want them to post answer at the same time). Thanks for answering!
Alright, there’s some steps that I may provide, but better get more opinions just in case. Here how you can pull this off without making it look like a slapped-together chatbot:
Custom Chat Feature Integration
If you want a front-and-center chat feature on your website that doesn’t scream “generic chatbot,” you’ll need to create a custom interface using OpenAI’s API. This lets you design the chat exactly how you want—clean, focused, and specialized for your topic.
Step 1: Get the API
Start by getting access to OpenAI’s API. Once you have the API key, it’s your golden ticket to building a highly customized feature.
Step 2: Design the Frontend
Forget the basic chatbot bubble. Build something sleek using HTML, CSS, and JavaScript. Position it where you want center stage
Step 3: Backend Logic
Set up a backend in Python (Flask or FastAPI), Node.js, or whatever you’re comfortable with. This backend will handle:
Sending user inputs to the API.
Receiving responses and displaying them on the website.
Step 4: Specialize the Model
Here’s the trick to making it topic-focused: prompt engineering. Every time the user sends a message, prepend a specific instruction for the model, like:
“You are an expert in [insert topic]. Only provide advice related to this area and ensure it’s concise and actionable.”
You can also fine-tune the model if you want to go the extra mile.
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Text-to-Image Generation
So, you also want to generate images on top of all this? Ambitious. But fine, here’s how:
Use DALL·E or Similar
Leverage OpenAI’s DALL·E API for generating images. When the user sends a query, route their input to both ChatGPT (for text) and DALL·E (for images).
How to Display Responses Simultaneously:
Parallel Requests: Send API calls for both ChatGPT and DALL·E at the same time.
Asynchronous Loading: While waiting for both outputs, show a loading spinner or placeholder to keep things smooth.
Combine Outputs: Once the responses come in, display the chat reply alongside the generated image in a neat layout
What if I don’t want AI generated photos? I want real web photos
You will have to set up your own gallery with an API backend and through actions link it with GPT. A standard GPT cannot reproduce images.
Also welcome I moved you to ChatGPT tag and added API into sub tag 
Another small thought:
If you want real web photos, then you need to define what “real” is.
Example:
Otherwise, AI-generated images from the web may be used.
Real web photos? Can you provide me a example of what you really what? I can explain better if I know exactly what you looking for
like for example when I search the web for ‘black hole’ the images that can be found on the web. Those images I want to put on my web. Is that possible?
So you what use a page as search engine? Probably it is , but will be not a easy task. Probably you need find some experience people on that area
They can’t farm images off internet without agreement with a search provider. That’s what the new search for GPT4o they did an agreement with a media company.
@gdfrza The task is really a bit unclear, but I don’t think it’s a direct “search engine”:
@sernadaandrea2002 I understood it to mean that an AI is to be integrated into the website as support.
No AI-generated images should be used for the images.
Well, this can be difficult, especially with the “black hole” example.
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Challenge: It’s difficult to completely avoid AI-generated images since many images on the internet are not clearly labeled as such.
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Proposed Solution: Create your own gallery with selected and verified images. This gallery can then be used by the AI to ensure that only the desired images are used.
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Advantages:
- Control over image sources.
- Avoidance of unwanted AI-generated images.
- Ensuring the quality and relevance of images.
Additionally, for generating images based on text inputs and posting them simultaneously with the chat responses, you can integrate an image generation API alongside ChatGPT. This way, you can provide a seamless experience where both text and images are generated and displayed together.
can you suggest what can I use for image generation API?
I did some quick research:
- OpenAI DALL-E 2: Known for generating highly realistic images from text descriptions.
- DeepAI: Offers a variety of pre-trained models and APIs for natural language processing and computer vision tasks.
- Stable Diffusion: A latent text-to-image model that generates realistic images based on text inputs.
- Bannerbear: Allows for the generation of images in various formats and provides a simple REST API for modifying image attributes.
- Eden AI: Provides a standardized API that gives access to various text-to-image generation providers like DeepAI, OpenAI DALL-E 2, and Stable Diffusion.
You will need to use something like Photoview or Lychee.
Lychee is a good choice IMO.
It is a self-hosted photo management system, it provides an API for tasks like uploading photos, managing albums, and retrieving metadata. Accessible at your Lychee domain (e.g., http://your-lychee-domain.com/api/), the API supports authentication for secure interactions and is documented comprehensively on the official Lycheedocumentation site. To use the API, ensure your installation is configured correctly and include authentication tokens in requests when required. For example, to upload a photo, send a POST request to /api/Photo::add with parameters such as the image file and album ID. This API enables seamless integration of Lychee’s photo management features into custom applications, automating workflows, and enhancing usability.
These can be used for your gallery. If you want to just generate images any image generator API will work I like dalle.
You can back end it into a GPT using Actions
Omg thank you for this, I’ll try this tomorrow
So happy I could help.
Hello, I’m trying to build an AI assistant for our website that feels native to the product.
I initially built this using the OpenAI Agent SDK and ChatKit, but I’ve run into several challenges while moving toward a production-ready experience. I’m now looking for advice from anyone who has solved similar problems.
Challenges I’ve faced with Agent SDK + ChatKit:
1. Production reliability
The agent works well in testing, but with real users I’m seeing inconsistent behavior:
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Unpredictable tool calls
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Responses that don’t always match the conversation context
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Cases where the agent fails to follow without a recovery path
2. UI Customisation limiation
My goal is to create an AI assistant that feels like a natural part of the product experience, not a chat box added on top.
Even with ChatKit customization options, I’m finding it difficult to make the experience fully match our product UI and workflows.
Has anyone successfully extended ChatKit beyond the default chat interface?
3. Limited support and debugging experience
I’ve struggled to find reliable support resources when running into issues.
I tried using the documentation AI assistant, but it frequently fails with errors like “The docs agent couldn’t complete the request. Please retry.” I also tested some official examples and ran into similar issues.
4. Knowledge base management and improving over time
Our knowledge base is maintained by multiple team members, which creates challenges:
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Outdated policies mixed with updated information
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Multiple documents containing slightly different answers
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No clear visibility into why the agent selected a specific answer
I’m looking for ways to build systems where teams can understand agent decisions, identify knowledge gaps, and continuously improve responses.
5. Action-oriented AI assistant
Beyond answering questions, I want the assistant to take actions, such as:
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Navigating users to specific areas of the product
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Triggering workflows
I’d really appreciate any advice you can share on building a reliable system and resolving these issues.
Hi @Scooby-doo ,
Welcome to the OpenAI community.
I would approach this across three layers: agent reliability, knowledge/action management, and the product experience layer.
One of the biggest challenges when moving an agent to production is reliability. It can work well during testing, but real users interact with it in many different ways. Unexpected questions, long conversations, missing context, unclear requests, and outdated or conflicting information can all cause the agent to give inconsistent responses. Understanding where these issues happen and why they happen is usually the first step toward improving the agent over time.
For the UI experience, ChatKit can be customized beyond a basic embedded chat box. It supports themes, widgets, and configuration options to better match your product experience. The customization options are documented here: ChatKit documentation
If the goal is an assistant that feels native to the product, consider combining ChatKit with your own product components. The assistant can trigger native UI experiences, guide users through workflows, open specific sections of the application, and provide contextual interactions instead of only returning text.
For action-oriented assistants, ChatKit supports interactive widgets and actions that can trigger client-side or server-side functionality. This enables experiences like navigation, workflow execution, and product actions. The important part is designing these actions as controlled tools with clear permissions and validation: ChatKit actions
OpenAI provides vector storage capabilities, but it does not currently provide a complete built-in knowledge management pipeline for this workflow. You would need to build the additional layers yourself to manage the knowledge lifecycle.
If you want to build a highly customized experience yourself, Codex can be very useful. You can describe the complete product experience, workflows, APIs, and UI behavior you want, then use it to generate and iterate on the implementation.
There are also no-code and low-code AI agent platforms that can help when you need production capabilities without building every layer yourself. Tools like YourGPT provide AI agent capabilities with knowledge management, action-based workflows, integrations, and customization options for building product-focused AI assistants.
The best approach depends on how much control you need. If you need full flexibility, building with the Agent SDK and your own stack gives you more control. If you want to move faster with built-in agent capabilities, no-code or low-code platforms can reduce the amount of infrastructure you need to build and maintain.
Thank you for the detailed response. However, I am already using Codex to build it, and I am currently encountering issues where it keeps looping through the same problems again and again—this is truly frustrating.
If you encounter any difficulties during the process, you can ask the Codex agent to search the web or try switching models, as either approach may prove helpful.
Do you have any programming experience?