Strategies for Deploying LLM-Driven AI Applications

Embarking on the adventure of crafting AI applications utilizing Large Language Models (LLMs) unveils a realm of possibilities while simultaneously presenting a complex maze of challenges when transitioning from fundamental prototypes to applications ready for production. In the last 6 months, at GoML, we have been working with end customers, both enterprise & mid-size, as well as our numerous partners in the GenAI space, which has helped us understand the nuances of building LLM-powered use cases, which bring actual business values to the clients.

Refer to our repository of 50+ LLM-powered, business-ready use cases here.


Essential Aspects in Developing LLM Applications


1. Strategic Training

The limitations of off-the-shelf models become apparent as LLMs become a pivotal part of operations, necessitating eventual Finetuning, RAG, or Prompt Engineering. By employing integrated data pipelines, genuine user queries can be efficiently logged and converted into clean training datasets, fostering continuous learning. Early investments in tools for dataset generation and model fine-tuning ensure that LLMs consistently deliver peak value by remaining in sync with evolving business needs.

2. Ensuring Observability

The journey from designing prompts that excel with test data to encountering challenges upon live deployment is a familiar one. Ineffective LLM management can lead to issues like extended wait times and unsuitable responses, adversely affecting user experience and potentially your brand. A well-thought-out strategy for LLM operations’ observability and monitoring is vital to quickly identify and resolve issues, involving tracing, conversation tracking, replaying, and gradual refinement.

At GoML, having productionized multiple LLM-powered use cases, we have built solutions to help improve the observability of these LLMs. One such solution, GoML LLM Visualize, helps visualize the LLM spends & the API endpoint hits as continuously updated dashboards.

See how LLM Visualize works here

3. Mastery in Prompt Engineering

With 50+ business-ready use cases built, at GoML we have observed that 90% of these use cases can be solved with faster & less complex approaches like Retrieval Augmented Generation (RAG) or effective Prompt Engineering, not needing complex finetuning. Under such observations, achieving reliable performance from Large Language Models (LLMs) crucially depends on the meticulous formulation of prompts, which necessitates a multi-faceted approach. Efficient storage and retrieval mechanisms for prompts are paramount to facilitate swift iterations, ensuring that the models can access and utilize them effectively and promptly.

Moreover, enabling prompt experimentation is vital, which can be realized through various methods such as A/B testing and user segmentation, thereby allowing for evaluating and optimizing different prompts in diverse user contexts.

Furthermore, fostering a collaborative environment is indispensable, ensuring that stakeholders can significantly contribute to prompt engineering, thereby leveraging collective expertise and insights to refine and enhance the efficacy and applicability of the prompts across various use cases and scenarios.

4. Cost Management

LLM API usage costs can escalate swiftly and must be managed judiciously. Instances where a minor parameter tweak has surged costs by 25% overnight are not uncommon. Implementing a meticulous API usage and billing tracking system, ensuring visibility into LLM costs, and establishing custom budgets and alerts are crucial for effectively managing expenditures.

Again, GoML LLM Visualize helps you keep a check on the costs by clearly visualizing them at a granular level.

5. Thorough Evaluation

Employing rigorous evaluation using datasets and metrics ensures reliability in LLM applications. Utilizing a centralized dataset store allows relevant datasets to be effortlessly logged from actual application queries and used for regular evaluation of production models. Integration with open-source evaluation libraries simplifies the assessment of vital metrics like accuracy and response consistency.

Furthermore, utilizing techniques like Chain of thought and chain of Verification (CoVe) helps get better outcomes from the LLM by creating a chain of prompts to better the output and reduce the hallucinations and incorrect responses given by the LLMs.


Additional Considerations for Developing LLM-Powered Applications: A Closer Look


  • Optimizing Performance:
    • Objective: Minimize response times and latency.
    • Strategy: Customize prompt chains to maximize throughput and ensure swift responses.
  • Embracing Multi-Model Support:
    • Objective: Ensure scalable and maintainable applications.
    • Strategy: Implement a unified, abstracted methodology to utilize various models like GPT-3.5, GPT-4, and Claude.
  • Prioritizing User Feedback:
    • Objective: Continuously enhance user experience.
    • Strategy: Implement mechanisms to capture and analyze real usage data and feedback, enabling iterative improvements.
  • Ensuring Enterprise Readiness:
    • Objective: Meet the demands of your target market with enterprise-grade capabilities.
    • Strategy: Incorporate elements like fine-grained access controls, reliable SLAs, robust data security, and automated testing frameworks to ensure reliability and compliance.


Each of these aspects plays a pivotal role in refining the development and deployment of LLM-powered applications, ensuring they are not only robust and reliable but also user-friendly and scalable.


Discover goML LLM Visualize: Your Gateway to LLMOps

GoML LLM Visualize, an open-source (Apache 2.0) LLMOps platform, addresses various facets like prompt management, versioning, instant delivery, A/B testing, fine-tuning, observability, monitoring, evaluation, and collaboration. Regardless of your stage in the LLM adoption journey, consider integrating LLM Visualize – it takes merely a minute and promises boundless value.

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