In this guide, we breakdown the Large Language Model (LLM) product development process. You'll learn all the necessary stages of developing an LLM-based product, including planning, execution, and monitoring.
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 of your input prompt is pivotal. By thoughtfully designing this prompt, businesses can generate countless descriptions that are not only unique to each product but also consistently adhere to the brand's tone, style, and guidelines.
Here's the breakdown of the ideal three-part input structure:
Brand Guidelines (Constant): This section forms the backbone of every product description. By embedding your brand guidelines into the prompt, you ensure that each generated description remains faithful to your brand's voice, tone, and values. For instance, the guidelines might instruct the LLM to maintain a certain tone (e.g., technical, precise), emphasize specific details, or follow a particular format.
Product Details (Variable): This is the dynamic section of the prompt, tailored for each product. By substituting placeholders such as {Product Name} and/or {List of Features}, businesses can automate the description generation for a vast array of products.
Instruction (Constant): A consistent directive informs the LLM about the exact task, such as creating a one-paragraph product description. This ensures the output meets the desired format and adheres to the brand guidelines every single time.
The advantage of this modular prompt structure is its scalability. Once an organization has established its brand guidelines and instruction segments, it can easily slot in different product details to churn out descriptions in bulk. The constant sections (1 and 3) ensure brand consistency, while the variable section (2) ensures product specificity. This systematic approach provides a streamlined solution, ensuring both efficiency and quality in product description generation.
And here's an example of what we would expect from the product description:
Pro tip: Although many companies wish to create a completely automated system, it is important to have a human monitoring the system to ensure quality output.
3. Model deployment
Once satisfied with the model's demonstrated proficiency and effectiveness in producing desired outcomes, the next step involves the strategic deployment of the model onto the platform of choice.
The goal of this stage
This step marks the transition from theoretical concept to real-world utility, enabling the integration of the model's capabilities into the intended environment. The overarching goal is to have the model working on the desired platform, so that the end user can utilize it.
Referring back to our product description generator example, at this stage, the company will integrate the product description generator into their internal systems. This will make it accessible to the people tasked with creating product descriptions.
Tasks to be completed at this stage
Deploying an LLM onto the desired platform involves a streamlined process which can be done through a number of different methods: connecting the model directly through an API, utilizing an SDK, or leveraging a cloud server. AI21 Labs models are available through Amazon SageMaker, Amazon Bedrock, and Google Cloud Platform. This makes the models easily accessible to any company using any of those providers.
By integrating with the LLM provider's API, you establish a direct communication channel between your platform and the model. This enables seamless transmission of input queries and retrieval of model-generated responses. Additionally, leveraging a cloud server, such as those offered by major cloud providers, allows you to host the LLM and make it accessible over the internet. This cloud-based deployment ensures scalability, enabling your platform to handle varying workloads efficiently.
Pro tip: Ensure that your final product undergoes a complete quality assurance (QA) and testing process before launch.
4. Monitoring results
The final stage of this process is to monitor the released product and analyze its performance.
The goal of this stage
The main aim is to keep a close eye on the product's technical performance as well as what impact it's having on the business.
The goal is to understand how users are reacting, how much they're engaging with the product, and receive and analyze their feedback. This is also the time to improve, expand, or grow the product. If the results aren't as expected, this is the time to examine what is and isn’t working, and act accordingly.
At this stage, the ecommerce company, as discussed in our example, should start using the generator to create product descriptions. It should also monitor results. This includes observing how users are reacting to the updated product descriptions and whether they are seeing an increase, decrease or no change in their sales.
Further improvements
To ensure the optimal performance of a product, maintaining a consistent line of communication with the LLM vendor is imperative. This ensures the seamless integration of any new models and capabilities post-product launch. In addition, it is critical to conduct periodic tests, so that any issues can be addressed promptly. In the event of any problems, early communication with the LLM vendor is of paramount importance. This facilitates timely diagnosis of the problem's root cause and the development of a feasible solution. Such proactive engagement with the LLM vendor aligns with the philosophy of continuous improvement and ensures that the product consistently meets its intended standards.
Conclusion
In the dynamic landscape of modern business, LLM-based solutions have surged in significance. However crafting an LLM-driven product requires nuanced understanding and meticulous planning. This journey encompasses careful preparation, precise model construction, strategic deployment, and vigilant monitoring. As the LLM-based product evolves, continuous learning and adaptation remain essential.
To learn more about LLM product development, contact our team at AI21 Studio.
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In this guide, we breakdown the Large Language Model (LLM) product development process. You'll learn all the necessary stages of developing an LLM-based product, including planning, execution, and monitoring.
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 of your input prompt is pivotal. By thoughtfully designing this prompt, businesses can generate countless descriptions that are not only unique to each product but also consistently adhere to the brand's tone, style, and guidelines.
Here's the breakdown of the ideal three-part input structure:
Brand Guidelines (Constant): This section forms the backbone of every product description. By embedding your brand guidelines into the prompt, you ensure that each generated description remains faithful to your brand's voice, tone, and values. For instance, the guidelines might instruct the LLM to maintain a certain tone (e.g., technical, precise), emphasize specific details, or follow a particular format.
Product Details (Variable): This is the dynamic section of the prompt, tailored for each product. By substituting placeholders such as {Product Name} and/or {List of Features}, businesses can automate the description generation for a vast array of products.
Instruction (Constant): A consistent directive informs the LLM about the exact task, such as creating a one-paragraph product description. This ensures the output meets the desired format and adheres to the brand guidelines every single time.
The advantage of this modular prompt structure is its scalability. Once an organization has established its brand guidelines and instruction segments, it can easily slot in different product details to churn out descriptions in bulk. The constant sections (1 and 3) ensure brand consistency, while the variable section (2) ensures product specificity. This systematic approach provides a streamlined solution, ensuring both efficiency and quality in product description generation.
And here's an example of what we would expect from the product description:
Pro tip: Although many companies wish to create a completely automated system, it is important to have a human monitoring the system to ensure quality output.
3. Model deployment
Once satisfied with the model's demonstrated proficiency and effectiveness in producing desired outcomes, the next step involves the strategic deployment of the model onto the platform of choice.
The goal of this stage
This step marks the transition from theoretical concept to real-world utility, enabling the integration of the model's capabilities into the intended environment. The overarching goal is to have the model working on the desired platform, so that the end user can utilize it.
Referring back to our product description generator example, at this stage, the company will integrate the product description generator into their internal systems. This will make it accessible to the people tasked with creating product descriptions.
Tasks to be completed at this stage
Deploying an LLM onto the desired platform involves a streamlined process which can be done through a number of different methods: connecting the model directly through an API, utilizing an SDK, or leveraging a cloud server. AI21 Labs models are available through Amazon SageMaker, Amazon Bedrock, and Google Cloud Platform. This makes the models easily accessible to any company using any of those providers.
By integrating with the LLM provider's API, you establish a direct communication channel between your platform and the model. This enables seamless transmission of input queries and retrieval of model-generated responses. Additionally, leveraging a cloud server, such as those offered by major cloud providers, allows you to host the LLM and make it accessible over the internet. This cloud-based deployment ensures scalability, enabling your platform to handle varying workloads efficiently.
Pro tip: Ensure that your final product undergoes a complete quality assurance (QA) and testing process before launch.
4. Monitoring results
The final stage of this process is to monitor the released product and analyze its performance.
The goal of this stage
The main aim is to keep a close eye on the product's technical performance as well as what impact it's having on the business.
The goal is to understand how users are reacting, how much they're engaging with the product, and receive and analyze their feedback. This is also the time to improve, expand, or grow the product. If the results aren't as expected, this is the time to examine what is and isn’t working, and act accordingly.
At this stage, the ecommerce company, as discussed in our example, should start using the generator to create product descriptions. It should also monitor results. This includes observing how users are reacting to the updated product descriptions and whether they are seeing an increase, decrease or no change in their sales.
Further improvements
To ensure the optimal performance of a product, maintaining a consistent line of communication with the LLM vendor is imperative. This ensures the seamless integration of any new models and capabilities post-product launch. In addition, it is critical to conduct periodic tests, so that any issues can be addressed promptly. In the event of any problems, early communication with the LLM vendor is of paramount importance. This facilitates timely diagnosis of the problem's root cause and the development of a feasible solution. Such proactive engagement with the LLM vendor aligns with the philosophy of continuous improvement and ensures that the product consistently meets its intended standards.
Conclusion
In the dynamic landscape of modern business, LLM-based solutions have surged in significance. However crafting an LLM-driven product requires nuanced understanding and meticulous planning. This journey encompasses careful preparation, precise model construction, strategic deployment, and vigilant monitoring. As the LLM-based product evolves, continuous learning and adaptation remain essential.
To learn more about LLM product development, contact our team at AI21 Studio.
ABOUT THE AUTHOR
Yotam Shinan
Developer Advocate
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In August 2021 we released Jurassic-1, a 178B-parameter autoregressive language model. We’re thankful for the reception it got – over 10,000 developers signed up, and hundreds of commercial applications are in various stages of development. Mega models such as Jurassic-1, GPT-3 and others are indeed amazing, and open up exciting opportunities. But these models are also inherently limited. They can’t access your company database, don’t have access to current information (for example, latest COVID numbers or dollar-euro exchange rate), can’t reason (for example, their arithmetic capabilities don’t come close to that of an HP calculator from the 1970s), and are prohibitively expensive to update. A MRKL system such as Jurassic-X enjoys all the advantages of mega language models, with none of these disadvantages. Here’s how it works.
Compositive multi-expert problem: the list of “Green energy companies” is routed to Wiki API, “last month” dates are extracted from the calendar and “share prices” from the database. The “largest increase“ is computed by the calculator and finally, the answer is formatted by the language model.
There are of course many details and challenges in making all this work - training the discrete experts, smoothing the interface between them and the neural network, routing among the different modules, and more. To get a deeper sense for MRKL systems, how they fit in the technology landscape, and some of the technical challenges in implementing them, see our MRKL paper. For a deeper technical look at how to handle one of the implementation challenges, namely avoiding model explosion, see our paper on leveraging frozen mega LMs.
A further look at the advantages of Jurassic-X
Even without diving into technical details, it’s easy to get a sense for the advantages of Jurassic-X. Here are some of the capabilities it offers, and how these can be used for practical applications.
Reading and updating your database in free language
Language models are closed boxes which you can use, but not change. However, in many practical cases you would want to use the power of a language model to analyze information you possess - the supplies in your store, your company’s payroll, the grades in your school and more. Jurassic-X can connect to your databases so that you can ‘talk’ to your data to explore what you need- “Find the cheapest Shampoo that has a rosy smell”, “Which computing stock increased the most in the last week?” and more. Furthermore, our system also enables joining several databases, and has the ability to update your database using free language (see figure below).
Jurassic-X enables you to plug in YOUR company's database (inventories, salary sheets, etc.) and extract information using free language
AI-assisted text generation on current affairs
Language models can generate text, yet can not be used to create text on current affairs, because their vast knowledge (historic dates, world leaders and more) represents the world as it was when they were trained. This is clearly (and somewhat embarrassingly) demonstrated when three of the world’s leading language models (including our own Jurassic-1) still claim Donald Trump is the US president more than a year after Joe Biden was sworn into office. Jurassic-X solves this problem by simply plugging into resources such as Wikidata, providing it with continuous access to up-to-date knowledge. This opens up a new avenue for AI-assisted text generation on current affairs.
Who is the president of the United States?
T0
Donald Trump
GPT-3
Donald Trump
Jurassic-1
Donald Trump
Google
Joe Biden
Jurassic-X
Joe Biden is the 46th and current president
Jurassic-X can assist in text generation on up-to-date events by combining a powerful language model with access to Wikidata
Performing math operations
A 6 year old child learns math from rules, not only by memorizing examples. In contrast, language models are designed to learn from examples, and consequently are able to solve very basic math like 1-, 2-, and possibly 3- digit addition, but struggle with anything more complex. With increased training time, better data and larger models, the performance will improve, but will not reach the robustness of an HP calculator from the 1970s. Jurassic-X takes a different approach and calls upon a calculator whenever a math problem is identified by the router. The problem can be phrased in natural language and is converted by the language model to the format required by the calculator (numbers and math operations). The computation is performed and the answer is converted back into free language. Importantly (see example below) the process is made transparent to the user by revealing the computation performed, thus increasing the trust in the system. In contrast, language models provide answers which might seem reasonable, but are wrong, making them impractical to use.
The company had 655400 shares which they divided equally among 94 employees. How many did each employee get?
T0
94 employees.
GPT-3
Each employee got 7000 stocks
Jurassic-1
1.5
Google
(No answer provided)
Jurassic-X
6972.3 X= 655400/94
Jurassic-X can answer non-trivial math operations which are phrased in natural language, made possible by the combination of a language model and a calculator
Compositionality
Solving simple questions might require multiple steps, for example - “Do more people live in Tel Aviv or in Berlin?” requires answering: i. What is the population of Tel-Aviv? ii. What is the population of Berlin? iii. Which is larger? This is a highly non-trivial process for a language model, and language models fail to answer this question (see example). Moreover, the user can’t know the process leading to the answers, hence is unable to trust them. Jurassic-X can decompose such problems into the basic questions, route each to the relevant expert, and put together an answer in free language. Importantly, Jurassic-X not only provides the correct answer but also displays the steps taken to reach it, increasing the trust in the system.
Do more people live in Tel Aviv or in Berlin?
T0
Berlin
GPT-3
There are more people living in Tel Aviv than in Berlin.
Jurassic-1
Berlin and Tel Aviv are roughly the same size
Google
(First hit is a comparison between Tel Aviv and Berlin)
Jurassic-X
More people live in Berlin than in Tel-Aviv
[‘Return population of Tel Aviv’; Return population of Berlin’; Return which is bigger between #1 and #2’] Step 1: Population of Tel Aviv. Result - 451523. Step 1: Population of Berlin. Result - 3664088. Step 3: Which is bigger, #1 or #2. Result - Berlin.
Jurassic-X breaks down compositional questions, answers the basic sub-questions, and puts together the answer. Importantly, this process is transparent to the user greatly increasing the trust in the system
Dynamic information (like weather and currency exchange rates)
Certain types of information change continuously - weather, currency exchange rates, share values and more. Such information will never be captured by language models, yet can easily be handled by Jurassic-X by integrating it with a reliable source of information. We performed a proof-of-concept on two such features - weather and currency exchange rates, and the design enables quick integration with more sources to solve your use-case. Weather - a loosely phrased question about the weather elicits an answer from all language models, where language models always return the same answer, regardless of when the question was asked (funny, right?), while Jurassic-X provides an answer based on the actual weather prediction.
I’m going to be in New-York in 3 days. Should I pack my umbrella?
T0
No
GPT-3
Yes, you should pack your umbrella.
Jurassic-1
Yes, you should. The weather forecast is rain.
Google
(Links to weather websites)
Jurassic-X
Yes, you should pack your umbrella, because in New York in 3 days there will be broken clouds and the temperature will be -2 degrees.
Currency exchange rates change much faster than weather predictions, yet the Jurassic-X concept - a language model connected to a reliable source of information - easily solves this problem as well.
How much Moroccan money will I get for 100 bucks?
T0
125 dirhams
GPT-3
100 moroccan dirhams is about 27$.
Jurassic-1
How much is 100 dollars in moroccan money?
Google
100 dirhams = 10.75 dollars
Jurassic-X
100 USD = 934.003 MAD
Jurassic-X combines a language model with access to APIs with continuously changing information. This is demonstrated for weather forecasts and currency exchange rates, and can easily be extended to other information sources
Transparency and trust
Transparency is a critical element that is lacking in language models, preventing a much wider adoption of these models. This lack of transparency is demonstrated by the answers to the question - “Was Clinton ever elected as president of the United States?”. The answer, of course, depends on which Clinton you have in mind, which is only made clear by Jurassic-X that has a component for disambiguation. More examples of Jurassic-X’s transparency were demonstrated above - displaying the math operation performed to the user, and the answer to the simple sub-questions in the multi-step setting.
Was Clinton ever elected president of the United States?
T0
Yes
GPT-3
No, Clinton was never elected as president of the United States.
Jurassic-1
No
Google
Clinton was elected president in the 1992 presidential elections…
Jurassic-X
Bill Clinton was elected president.
Jurassic-X is designed to be more transparent by displaying which expert answered which part of the question, and by presenting the intermediate steps taken and not just the black-box response
Your Turn
That's it, you get the picture. The use cases above give you a sense for some things you could do with Jurassic-X, but now it's your turn. A MRKL system such as Jurassic-X is as flexible as your imagination. What do you want to accomplish? Contact us for early access
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