Just admit it: you can hardly name an industry that has undergone as many transformations as eCommerce did in recent years. Since the COVID-19 pandemic has led to entirely different consumer behavior, and now the technologies are only accelerating the changes.

The rise of AI-empowered tools has marked a new era. Businesses can already build a customer-centric online store with AI, and buyers make more informed purchasing decisions while Chat GPT improves their customer experience. And you cannot lag in this fierce race; otherwise, you’ll lose your audience: two-thirds of online shoppers claim to leave the e-store (even their favorite one) if they don’t find what they need fast. 

Yes, Chat GPT (and other Large Language Models for commercial use) can show great results here by engaging customers with human-like conversational behavior. However, even the most powerful technology is worth nothing without 2 things:

• The appropriate way to leverage it;
• The relevant context.

The first point is achievable: you try, you learn, you train, test, improve, and repeat. 

The second one is only possible with up-to-date information. And here comes the problem because, as you might know, the bold Chat GPT doesn’t give the most recent data, which diminishes all previous efforts in building a smooth customer experience. 

We at UnidataLab are constantly addressing and solving such issues. This time, we experimented with the tool that cracks the request-context-reply concern. And we’ll show you how it can help eCommerce businesses by fetching and extracting the most recent data, potentially decreasing the churn rate. But let’s start with the basics.

What Is LangChain?

Emerging as a game-changing technology, large language models (LLMs) are revolutionizing developers’ capabilities, enabling them to create previously elusive applications. However, as we already said, relying solely on LLMs is often inadequate for harnessing the full potential of these applications. The true power emerges when you combine LLMs with other sources of computation or knowledge. 

And here’s the time for the LangChain library to enter the game. It aims to provide comprehensive support to facilitate the development of such applications. The library offers assistance in the following:

❓ question answering over specific documents; 

💬 chatbots;

🤖 agents. 

Agents, in particular, involve LLMs in:

1. making decisions; 
2. taking actions; 
3. observing outcomes;
4. iterating until completion. 

To streamline the process, LangChain offers a standardized interface for agents, various agent options, and practical examples of end-to-end agents.

How It Works

The library applies the Chain of Thought (CoT), a prompting technique that encourages the model to generate a series of intermediate reasoning steps. This approach offers several appealing attributes.

1. The technique enables models to break down complex problems into manageable intermediate stages, allowing for the allocation of additional computation to tasks that require more reasoning steps. 

2. It provides transparency into the model’s behavior, shedding light on its decision-making process and aiding in identifying and resolving potential reasoning errors. Although fully understanding the model’s computations supporting a given answer remains challenging, this technique offers interpretability.

3. This type of reasoning applies to tasks such as math word problems, commonsense reasoning, and symbolic manipulation, encompassing various challenges that can be tackled through language-based solutions. 

4. Eliciting chain-of-thought reasoning in large off-the-shelf language models is relatively straightforward by incorporating examples of such sequences in the few-shot prompting exemplars.

The experiment employs sequential chains, which are chains that run their links in a predefined order. For example, suppose we want to ask the question: “Recommend me a smartphone with a low price” with the dataset below. In that case, it will run this chain:

In all cases, firstly, it processes the dataset using pandas utilities to extract the desired item from the database and do it until it reaches the final answer and then gives it to the user.

The Experimental Part

For the experiment, we place a relevant database with products at the input and then drop different prompts on them. This way, we simulate customers’ requests through a chatbot on an eCommerce website to give clear and personalized answers. In other words, we created a database of Amazon products for the experiment. However, we can build something similar for your customized business needs. 

This is a dataset with 20 smartphones, collected from Amazon:

Related Posts

How to build an effective conversational search

Intelligent EdTech: How AI is reshaping education

Building Educational Helpdesk Chatbot with LangChain and ChatGPT: What You Need to Know

Document processing with AI: use cases and advantages