Developing a chatbot with LLMs | Our guide [Update 2025]
Imagine a world where every question you ask is answered instantly and accurately, where every request is handled efficiently, and where every interaction with a machine (such as a search engine) is personalized according to your preferences. This is not science fiction, but the reality offered by artificial intelligence (AI) and in particular by chatbots, which for the most part rely on increasingly sophisticated AIs.
Chatbots have revolutionized the way we interact with technology, transforming user experiences from passive to active, from generic to personalized. But how do these virtual assistants manage to understand and respond to our requests with such precision? In 2024, the answer lies in large language models (LLM).
As a reminder (if you have been living in a cave for almost 2 years), LLMs are models pre-trained on huge amounts of textual data, allowing them to understand and generate human language in a coherent and relevant way. However, for a chatbot to meet the specific needs of a task or user, these models must be specialized, enriched, and prepared to handle specific tasks.
If you're new to AI and machine learning, or if you're just curious to understand how these technologies work, you've come to the right place. In this article, we are going to unravel the mysteries of developing chatbots using artificial intelligence.
So are you ready to find out how to create your own chatbot? Follow the guide, and get ready to be amazed by the power and versatility of applied artificial intelligence technologies.
How to develop a chatbot with LLMs?
Developing a chatbot with LLMs (Large Language Models) involves carrying out the fine tuning of the model so that it can respond effectively to user requests. This process for enriching the LLM involves making specific adjustments to the pre-trained model so that it can understand and generate human language in a manner that is relevant and consistent in a given context (for example, related to a particular industry or field of education).
For Fine Tuner An LLM, we typically add additional data related to the specific tasks we want the chatbot to complete. This data may include sample conversations, frequently asked questions, pre-arranged answers, or any other type of information relevant to the task at hand. This additional data serves as a basis for learning the chatbot, allowing it to understand the nuances and subtleties of human language in the context of the task at hand.
By proceeding with Fine tuning, the language model becomes more specialized and learns to recognize key words and phrases associated with the task at hand. It also learns how to use these words and phrases appropriately (or more appropriately) in different situations, allowing the chatbot to provide more accurate and relevant answers. This makes the chatbot more useful and informative, giving the impression that it has deep expertise in a specific area.
It is important to test the chatbot after the task of Fine tuning to ensure that the changes made to the model are effective. Tests may include manual evaluations, automated tests, or a combination of both. The test results make it possible to measure the performance of the chatbot and to identify possible problems or errors. Data collected during testing can also be used to further improve the model and optimize its performance.
💡 In short, the Fine tuning of an LLM for a chatbot is an essential process for allow the chatbot to understand and respond effectively to user requests. By adding additional data related to the task at hand and by testing the chatbot, we can perfect an LLM and create a powerful and useful tool, in a multitude of business sectors.
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Is it possible to specialize an LLM?
Absolutely, specialising an LLM is not only possible, but it is a common practice to improve the performance of a language model. By introducing more relevant data into pre-trained models (such as ChatGPT), the model refines its understanding and the precision of its responses in specific contexts.
This process of”Fine tuning“adapts the general capabilities of an LLM to better suit specific sectors or tasks. It is thanks to this specialized training process that chatbots can go from simple functional tools to highly efficient (not to say “competent”) tools in the areas they cover.
The success of Fine tuning of LLMs is measurable through rigorous tests that assess the accuracy and usefulness of the chatbot, ensuring that the end product matches the intended user experience.
As an example, let's take the case of GPT-3.5. GPT-3.5 is an advanced language model developed by OpenAI. Thanks to its API, it is now possible to customize this model to meet the specific needs of each business or organization. OpenAI refers to this feature and refers to it as”Fine tuning“.
In this example, the Fine tuning involves training the GPT 3.5 model on data specific to a particular domain or task. For example, an e-commerce business can use fine-tuning to train GPT 3.5 to understand and answer customer questions about its products. By using examples of real conversations between customers and customer service, the business can refine the model to answer questions in a more accurate and relevant manner.
With the GPT 3.5 fine-tuning API, developers can customize the model in a simple and effective way. Tests have shown that custom models can even surpass GPT-4's core performance on some highly targeted tasks. Additionally, all data sent through the API is the exclusive property of the customer and is not used by OpenAI or any other organization to train other models.
Why do we have to specialize in LLMs to develop a chatbot?
The enrichment of large language models (LLMs) using specialized training data is essential for the development of chatbots. This allows chatbots to understand and converse in the context specific to their use.
Chatbots serve a variety of purposes, from customer service in banking to virtual assistance in healthcare. A chatbot designed for banking customers, for example, should understand financial jargon and respond appropriately to transaction queries.
The ability to refine LLMs with more training data is made viable because these models are intrinsically designed to learn from more data. When fed with sector-specific information, the model can begin to recognize the unique patterns and jargon of that field. As a result, the chatbot is becoming more “intelligent” and nuanced in its interactions. This customization is critical to providing accurate and relevant answers that add value for the end user.
In addition, a specialized chatbot is a powerful tool for businesses. It can handle numerous customer requests simultaneously, reduce response times, and operate 24/7.
This ability of the AI model to provide instant and reliable support improves customer satisfaction and loyalty. The return on investment when specializing LLMs as part of the development of a chatbot is clear: it leads to service improvements without a proportionate increase in costs (i.e.: the main cost to take into account is a fixed cost of data preparation and specialization training).
👉 In short, by investing in the specialization of LLMs for chatbots, companies ensure that they have a sophisticated digital assistant able to hold fluid conversations taking into account a context of use that reflects the knowledge and needs of a particular sector or service area.
How to prepare an LLM for a chatbot, step by step?
The Fine tuning of an LLM (Large Language Model) for a chatbot involves several steps designed to make the chatbot smarter and more effective in carrying out a specific task or area.
Follow this simple, step-by-step guide to help you set up a process for Fine tuning of effective LLM.
Step 1: Define your goals
Clarify what specific task you want the chatbot to do. Whether it's managing customer queries in retail or providing technical support, having clear goals helps tailor the training process to a specific task.
Step 2: Collect training data
Gather a data set that includes a wide variety of textual data and examples that are relevant to the chatbot's intended tasks. This data can include text generation, typical customer queries, industry- or domain-specific jargon, and appropriate responses.
Step 3: Choosing the right model size
Select an LLM size that balances model performance with your available computing resources. Larger models may be more powerful but require more computing resources.
Step 4: Pre-training on general language
Start with an LLM that has been pre-trained on broad linguistic data. This gives the chatbot a solid foundation in understanding natural language.
Step 5: Apply the techniques of Fine tuning
When refining the LLM, use artificial intelligence techniques such as Transfer Learning and Prompt Engineering to adapt the creative content of the chatbot to your specific use case. Provide textual data that reflects real requests and responses in your field.
Step 6: Adjust model parameters
Adjust LLM training parameters such as learning rates for better performance on your tasks. You can use learning rate planners or apply effective fine-tuning methods like LoRa or adapters.
Step 7: Test and Evaluate
Submit your chatbot Fine-Tuné to rigorous testing using new, unseen data. Evaluate its responses against “ground truth” data sets to ensure they are accurate and relevant.
Step 8: Monitor and Iterate
After deployment, continue to monitor chatbot performance. Gather feedback and incorporate it into future sessions Fine tuning to maintain and improve the relevance and accuracy of the chatbot.
Remember that creating a specialized and efficient model requires a balance between technical knowledge and understanding of specific needs of the user. It is always necessary to favor informative and creative content over natural interactions to offer the best possible user experience.
💡 Not sure where to start? No worries! Complete solutions exist for create your own chatbot easily, without the need for special technical skills.
What are the common challenges when specialising large language models?
Here, we've compiled some common challenges associated with specialising LLM in the context of chatbot development that you might face during the process of Fine tuning major language models. For each defined, we offer a solution that seems to us the most suitable. Have a look!
Shortage of quality training data
Challenge: Obtaining high-quality, domain-specific training data can be challenging. LLMs require a large volume of data to learn effectively, and if the available data is insufficient or not representative of real and specific use cases, the performance of the refined model may be suboptimal.
(Click to find out the solution)
Overfitting the model
Challenge: Refined models may work exceptionally well on training data, but fail to generalize to new, unseen data. An unknown occurrence or an inadequate configuration, or overadjustment of the model, can make a chatbot ineffective in practical applications.
(Click to find out the solution)
Balance between model size and computing resources
Challenge: There is often a trade-off between model size and available computing resources. Larger models tend to perform better, but require significantly more memory and processing power, which can be expensive and less environmentally sustainable.
(Click to find out the solution)
Ability to stay up to date with rapid advances in AI
Challenge: The field of AI and machine learning is progressing rapidly. Staying up to date with methodologies, models, and best practices is a challenge for AI practitioners.
(Click to find out the solution)
Safeguarding AI ethics and mitigating bias
Challenge: Bias in training data for AI can lead to inappropriate LLM responses, which may unintentionally spread stereotypes or discriminatory practices.
(Click to find out the solution)
How to assess the performance of a model Fine-Tuné ?
To ensure that the specialized LLM for your chatbot applications meets the goals you set with users, consider the following strategies to assess its performance:
Accuracy testing and real world testing
Compare the model results to a set of known and correct answers to determine its accuracy rate. This can include metrics like accuracy, recall, and F1 score. Use the chatbot in a real world scenario with real customer interactions to assess its practical performance.
A/B testing and error analysis
Implement A/B testing where some users interact with the refined model, and others with the base model. This may highlight improvements or issues introduced by the Fine tuning. Examine the types of mistakes that the chatbot makes to identify model parameters and areas for improvement.
User satisfaction surveys
Gather feedback directly from users about their experience interacting with the chatbot, focusing on both the quality of responses and the level of engagement.
Consistency checks and miscellaneous input assessment
Ensure that the chatbot's responses remain consistent across similar queries, indicating that it can reliably recognize patterns in human language. With this, test the chatbot with a wide range of different inputs, including varied linguistic constructs to ensure robustness in different scenarios.
Bias testing and resource use
Look for evidence of bias in chatbot responses and ensure ethical interactions with all user groups, as well as representativeness in your data sets. Identify and correct possible hallucinations in your models. Once done, monitor the computing resources used by the model during operation, ensuring that they match your capacity and efficiency goals.
Are pre-trained models sufficient for artificial intelligence chatbots?
Although pre-trained models, such as Large Language Models (LLMs), provide a substantial advantage in understanding natural language, whether they are adequate for unmodified AI chatbots depends on the specificity and complexity of the intended function of the chatbot model.
Let's consider a few key points:
General skills
Pre-trained models provide a solid foundation. They are trained on broad data sets that include a variety of textual data, giving them a deep understanding of natural language and the ability to generate coherent and contextually relevant text.
These abilities generally make them good at answering questions and handling a variety of tasks. NLP (for “Natural Language Processing”) as soon as they are put into production.
Specific tasks
For domain-specific tasks or specialized customer requests, a pre-trained model may not provide accurate answers without conducting a Fine tuning additional. This is because the model's training data may not have included enough examples from the required domain or context.
Fine tuning
The Fine tuning LLMs for chatbot applications is the process of adapting these pre-trained models with additional data sets that target a given domain.
This refinement helps adapt the performance of the AI model to recognize mechanisms in human language and knowledge that are specific to a business's needs and customer interactions.
Advanced techniques to improve the effectiveness of LLMs
Methods such as adapters or techniques such as Prompt Engineering and LoraConfig (low-rank adaptation), allow parts of the model to be targeted for adjustments without changing the entire model. This means that fewer computing resources are used and that adaptations can be made without the need to completely redesign the architecture of the model itself.
Last words
In the context of chatbot development, the customization of LLMs pre-trained by specialization and Fine tuning turns out to be a vital step in developing a conversational AI that is both competent and “aware” of the complexities in its field (we will of course not talk about human consciousness, but about mechanisms that allow the tool to generalize and to have a certain perspective).
While pre-trained models lay the groundwork for general natural language proficiency, the true potential of chatbots is unlocked when these large language models are meticulously tailored to the nuances of their intended tasks and user interactions. The journey to create sophisticated AI chatbots is full of challenges, but it is also full of opportunities for innovation and increased accessibility to artificial intelligence in business.
What do you think? If you want to know more, or are looking for annotators to prepare training data for your LLM, you can contact us ask for a quote at this address.
Additional resources:
- How to develop a Chatbot with Llama 2: 🔗 https://blog.streamlit.io/how-to-build-a-llama-2-chatbot/
- How to create a Chatbot with ChatGPT: 🔗 https://www.freecodecamp.org/news/how-to-create-a-chatbot-with-the-chatgpt-api/
- Create a Chatbot with ChatGPT and Zapier: 🔗 https://www.youtube.com/watch?v=l3Lbwwjdy8g
- DeepLearning.AI, Finetuning Large Language Models: 🔗 https://www.deeplearning.ai/short-courses/finetuning-large-language-models/
- Coursera - DeepLearning.AI course: 🔗 https://www.coursera.org/projects/finetuning-large-language-models-project
- Directory of AI tools that can be used to develop Chatbots: 🔗 https://dang.ai/
