Llm Product Development

The Complete Guide to LLM Product Development | AI21

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The current buzz around generative AI is overwhelming. The potential it holds is exciting, promising to revolutionize industries and redefine user experiences. However, successful implementation of generative AI requires a strategic approach, an understanding of the process, and a clear roadmap to navigate the complexities involved.

At AI21 Labs, we’ve developed Large Language Model (LLM) solutions for top enterprises like Ubisoft, Clarivate and Carrefour. Our custom development process generates predictable results and revenues for our clients (to learn more about this, feel free to contact us ).

Let’s break down this process into its basic stages, which require meticulous planning, precise execution, and vigilant monitoring.

1. Preparation: Plan your LLM development project

Like any successful project, an LLM development project needs to be thoroughly thought through, and well planned. The preparation stage is dedicated to mapping out the project’s overarching objectives and outlining the details of the strategic plan.

The goal of this stage

This phase can either be undertaken independently, by the company wanting to build the product, or in collaboration with the LLM provider it decides to work with.

During this stage of the process, it is important to collect the necessary resources, including the relevant data which will be used as context input, or as a dataset for an LLM training process. Some data may not be accessible to third party vendors, such as the LLM provider. It is therefore crucial to overcome these limitations at this stage.

For example, let’s take an ecommerce company that wants to create a tool that generates product descriptions for their website, based on a short list of product features. At this stage of the process, they should collect data such as a content style guide and a list of characteristics of each of their products and already existing product descriptions, which will be used as context input and output for the LLM during the training process. If, for instance, the company is unable to share some of this information with a third party vendor, perhaps because they are coming out with a new product whose details are still confidential, they should consider this issue at this stage, and seek a reasonable solution.

Identifying stakeholders

Stakeholders include strategic decision makers such as CEOs, CTOs and product managers. A product manager is usually a key player in this strategic phase, since they are well versed in both technical and business aspects.

Additionally, the people who will ultimately implement the solution must also be involved, in order to contribute their technical and practical expertise. These include data scientists, data engineers, software engineers, devops, and designers.

Furthermore, the legal and compliance team should be involved to address privacy concerns, in addition to business analysts and domain experts to assist in making the most informed strategic decisions.

2. Building the product

Once the preparation stage has been finalized, and the required results have been mapped out, it is time to build the model.

The goal of this stage

During this stage, the company’s goal is to implement all of the plans made in the preparation stage, to reach the desired outcome; namely, for the LLM model to accept a certain input and retrieve an appropriate output.

Tasks to be completed at this stage

1. Choosing a language model: This will be done with the help of the LLM provider. There are a number of different factors to consider based on the specific use case. These factors include cost, performance and the model’s ability to handle the use case complexity.

2. Defining the user flow and wireframes: This phase entails outlining the user’s interface with the tool being built, alongside determining the anticipated user-input data, its format, and the desired output. This step, if done correctly, will save a lot of time when developing and building your product.

3. Data Curation: This involves curating and preparing the necessary data for the specific use case. This can include examples of different types of inputs that the various components of the LLM will be provided with, as well as examples of desired outputs. This data will be used to test the model’s outputs at the model evaluation stage. If data security issues have not been solved yet, this is the time to determine which data can be shared, and find solutions to any data privacy issues.

4. Training / Prompt Engineering: This is a crucial task in which the company customizes the Language Model to fit their specific needs.

This can be done using one of two methods, or a combination of both.

The first is to further train the language model by feeding it examples of inputs and expected outputs. This allows us to learn the intricacies of specific language patterns. The second is prompt engineering. This involves crafting well-defined input queries, instructions, or context for a language model to provide accurate and relevant responses, optimizing its expected outputs for specific tasks or applications.

5. Model Adjustments (Parameters): Adjust the parameters to optimize response quality according to your specific needs. This step refines the model’s ability to understand and produce human-like responses, aligned with your product’s objectives. Read more about parameters here .

6. Model Evaluation: Assess your model’s performance against a diverse set of scenarios and benchmarks. This evaluation provides insights into its strengths, weaknesses, and areas for improvement, ultimately guiding you towards a polished end product.

7. Pre-processing and Post-processing: Pre-processing involves cleaning and structuring input data for the model, while post-processing polishes the model’s output, ensuring it’s coherent, grammatically sound, and aligned with your brand’s needs.

Structuring Prompts for Scalable Product Descriptions with LLMs

Referring back to our example of an ecommerce company developing a product description generator, at this stage, they must determine which language model to use. In this particular case, a foundation model would be most appropriate.

When aiming for automated, high-quality product descriptions, the structuring...