Building an LLM-powered application can be challenging, especially if you're new to the field. There are various steps involved in the process, from thinking of a feasible idea to deploying the application.

In this blog, we will go through each step involved in building LLM-based applications. I will try to cover all the challenges and best practices involved at each step based on my experience and knowledge gained. Let's get started!

Step 1: Use case Discovery

• You need to identify a problem that can be effectively solved using LLMs and significantly impacts your target audience.

We should leverage the LLM for its powerful language understanding and processing. It works well on summarisation, basic reasoning and content generation.

LLM is not everything about AI, and not all the AI use cases are suitable for Generative AI.

• For example, when you have the sales data for a particular product, it doesn't make sense to give that to LLM to ask what will be my sales for the next day. However, you may ask it to write the code to use some Non-Generative ML techniques to forecast the data. Therefore, we still use LLM on the "Content Generation" use case.

Step 2: Prototype Building [Optional]

• This step is all about validating your concept and understanding how it performs, so keep it simple to start.
• Begin with a pre-trained LLM and craft an effective prompt. Use libraries like Langchain, which provide tools and building blocks that simplify building LLM-powered applications with minimal code.
• Create a web app playground using Streamlit or Chainlit for an interactive showcase.
• Once built, present the prototype to stakeholders for feedback and validation, especially for business-focused solutions.

Step 3: LLM System Strategy

Break down the tasks and decide if a single LLM with prompt engineering is enough, or you need an agent or a multi-agent system. You can read this blog for a better understanding of llm agents and multi-agents.

1. Use a standalone LLM when the task is simple, can be done with one prompt, and doesn't need to interact with other systems.

2. Use an LLM agent when the task is complex but singular, and it needs to interact with other systems (e.g — user database)

3. Use a multi-agent system when the use case requires specialised expertise for different steps, involves complex workflows, or requires collaboration across multiple systems and tasks.

Example:

In the health domain, a standalone LLM can handle general health queries, while an LLM agent can schedule appointments by matching user needs with a doctor's expertise and availability stored in database. For more complex tasks, such as assisting physicians with decision-making, a multi-agent system could be employed, where one agent researches the latest medical studies, another summarises patient records and a third generates personalised treatment recommendations, providing a comprehensive decision-support tool for doctors.

Step 4: LLM Setup:

Choosing the right LLM is key to your application's success, and several factors come into play:

Open-source vs. Proprietary:

• Open-source LLMs (e.g., Llama3) are developed in a transparent, collaborative environment where the source code and weights are publicly available and free for anyone to use, modify, and enhance.
• Proprietary LLMs (e.g., Azure Open AI gpt-4o) are developed and managed by individual companies, with their source code, training processes, and data kept confidential.
• Choosing between them depends on factors like cost, performance, and data privacy.

Choose Open source LLM when : —

1 . You have the expertise to deploy and manage the LLM infrastructure.

2. You don't want to rely on a third-party service.

3. You have very sensitive data that can't leave your premises.

Choose proprietary models when: —

1. You don't want to manage the LLM infrastructure.

2. You can pay for the service.

3. You trust the provider with your data and not dealing with sensitive data.

Pre-trained vs. Fine-tuned:

• Pre-trained LLMs are trained on vast amount of data and it efficiently works in most of the cases.
• Fine-tuning is the process of taking a pre-trained model and further training it on a domain-specific dataset.
• If fine tuning done in the right way, it can tailor the model to have a significantly better performance for domain specific tasks.

Choose Fine-tuning when: —

1. You have substantial amount of labeled data specific to the task.

2. You can spend on cost of resources utilised in training.

3. You have the expertise to train and deal with challenges like overfitting and under fitting.

4. Your use case is highly domain specific.

As we know fine-tuning can boost LLM's capability but at the same time it brings lot of challenges, we can use few techniques like Few shot prompting (in the prompt we mention few user inputs and respective response, LLM would try to learn how to perform the task), Retrieval Augmented Generation (RAG) etc. to enable domain-context learning in pre-trained LLMs.

Accuracy vs. Latency:

• LLMs with higher accuracy often have higher latency, meaning they take longer to generate responses.

Choose LLMs with lower latency when you are using LLM system for real time user-facing applications such as help-desk chatbots.

Choose Higher accuracy models for complex tasks such as assisting researchers in understanding papers.

• For example, GPT-4o is great for multi-step tasks, while GPT-4o mini is faster for lightweight tasks.

Use-case Specific Models:

• Some LLMs are designed for specific use cases, such as code generation, summarisation, or translation.

We can even use different LLMs for various steps in our LLM system.

Note : We must always build our LLM Application in such a way that it is not coupled with which LLM we are using so that we always have room to use improved LLMs.

Step 5: LLM Hosting [If not pre-trained enterprise LLM]

If you are not using enterprise pre-trained LLMs and have either fine-tuned by training the LLM or using open source LLM, you will need to host the LLM to a server or cloud infrastructure so that it can be used for inference in your application. Below are the types of hosting each with its own advantages and challenges:

Private Hosting:

1. Full control over servers and environment.

2. Best for sensitive applications with data privacy needs.

3. Requires technical expertise and limited scalability.

Cloud Hosting:

1. Managed services from AWS, Google Cloud, Azure.

2. Handles scaling, load balancing, and security.

3. Flexible and scalable, ideal for most business needs.

Hybrid Hosting:

1. Combines private servers for sensitive data with cloud for scalability.

2. Balances control with flexibility.

3. Suitable for organisations needing both security and scalability.

Whether you prefer the control of private hosting or the convenience of managed cloud services, knowing your options ensures you choose the best fit for your needs.

Step 6: Development of the application

Transitioning from a proof of concept (PoC) to a production-grade LLM-powered application introduces several challenges, such as scaling, performance, and robustness.

• Build an architecture design showing all the components and their interaction using software designing best practices before starting the actual development.
• Below is a sample architecture built for a multi-agent RAG based LLM System.

Below are some points to consider : -

Manage Dependencies:

• We should use virtual environments to manage dependencies and maintain version consistency across development environments.
• This helps avoid version conflicts and ensures the application behaves consistently on different systems.

Prompt Construction:

• Crafting effective prompts for an LLM app involves a balance of clarity, specificity, and context. To understand more on effective prompt design, you can refer to this blog.
• Experiment with different prompts, tweak language, and systematically refine them based on the LLM's responses to achieve the best outcomes.
• Manage your prompt templates by making them versioned, decoupled from the application's core code and deployments, and easily traceable from a request perspective. Langfuse is one such prompt management tool.

Handling LLM API Rate Limits and Failures:

• While using enterprise LLM APIs, we may run into rate limits, so it is important to handle these gracefully.
• One such way is to cache the responses and use them to minimise redundant API calls. Also, these APIs may fail unexpectedly.
• To handle this, we can add a retry mechanism to ensure that the application is resilient to such failures, and also a way to notify the team in case of such failures to take necessary actions.

Content Moderation:

• Content moderation ensures LLM outputs are appropriate, respectful, and legally compliant.

LLM Responses must be filtered for harmful content before being sent to users.

In chatbot applications, user inputs also should be checked for harmful content before reaching the backend LLM system. If harmful content is detected, the system should alert the user and prevent further steps.

Comprehensive Testing:

• Write unit tests for individual components and integration tests for the entire system.
• Conduct load testing to ensure the system can handle expected traffic, security testing to protect sensitive data, and performance testing to evaluate speed and stability.

Serving LLM application as an API:

• Once our LLM system is up and running, we can expose it as a REST API for accessibility.
• The API wraps the complex LLM logic, allowing interaction through simple web requests. Therefore, it can be easily integrated with frontend UI or other systems.
• This approach supports scaling by deploying multiple instances of the API to handle increasing demand.

Key design considerations for the LLM system API building include:

1. Authentication for secure access.

2. Rate limiting to control API usage and prevent overload.

3. Clean response formatting for smooth user interactions.

• For example — We can use FastAPI to make our LLM system accessible in applications. Features such as, managing multiple requests concurrently, automatic type checking and validation etc. makes it an excellent choice.

Building User-Friendly Interface:

• A simple, intuitive UI ensures that non-technical users can easily interact with our LLM system API.
• Whether through text inputs or pre-defined options, the interface should make it easy to use and provide clear feedback on results.

Evaluate LLM Performance:

Regular evaluation helps maintain the LLM's effectiveness as your application grows.

Establish clear evaluation metrics and use tools like promptfoo to test and analyse prompts, generating performance reports.

• For example — Suppose we are building a user facing chatbot for answering queries. Here one such criteria can be that the response should always be polite. In this case we can use LLM as judge to test if the response is polite enough or not.
• For more insights, you can refer to my blog on evaluation of LLM-Powered applications.

Step 7: Observability Setup:

• Maintaining performance and reliability is crucial. Set up monitoring and alerting mechanisms to detect issues early and address them before they affect users.

Logging is essential for tracking system activities, errors, and user interactions. Monitor system metrics like response times, error rates, and resource utilisation.

• Tools like Prometheus can integrate with FastAPI to collect and track these metrics. Set up alerts for conditions like high error rates or increased response times to notify of issues. You can use Grafana with Prometheus to create real-time visualisations of metrics for system health monitoring.

Implement health check endpoints to monitor key components like vector databases and LLM model availability. Regular health checks ensure overall system functionality, and monitoring tools trigger alerts for failures.

Ensure robust error handling by catching exceptions, logging errors, and providing informative messages. This helps reduce downtime and allows the system to recover from failures smoothly.

In summary, a combination of logging, metrics monitoring, health checks, and proper error handling forms the foundation of a reliable, well-monitored LLM system.

Step 8: Deployment and Hosting in Production:

• Once the application is ready, its time to host it in production either using private hosting or cloud by considering the trade off.
• You can choose to package the application in Docker to simplify the deployment process and enable easy scaling of applications across different cloud platforms.
• Develop a CI/CD pipeline for deploying the application where automated tests are run, and LLM responses are evaluated against the metrics defined in development stage.

Conclusion :

I hope this guide helps you navigate the process of building LLM-powered applications and inspires you to build it for you. Thank you for reading this blog and feel free to ask questions and give feedback in comments. Happy coding!