LLM Productionization Difficulty Rubric

At Neural Bridge, we have traversed the complex landscape of Large Language Models (LLMs), bringing over 50 applications to fruition. This journey has revealed five pivotal categories that dictate the ease or complexity of LLM productionization. Here, we share our insights, drawing from our extensive experience across various domains and continents.

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Category 1: Deployment Type

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1.1 Any Cloud (Difficulty: Easy)

Deploying LLMs is a breeze if you're open to using any cloud or SaaS solution, like OpenAI, Anthropic, or Google. This route offers easy access to leading models with minimal engineering effort. It’s a popular choice among startups and smaller enterprises, though larger corporations may have reservations due to concerns like security and compliance.

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1.2 Virtual Private Cloud (Difficulty: Medium)

Many substantial enterprises operate within a virtual private cloud on platforms like AWS, Azure, or GCP. Incorporating LLMs into this environment is more complex. Providers like AWS (with Anthropic), Azure (through OpenAI), and Google (with their Palm/Gemini model) are beginning to offer LLM services within these private clouds, addressing compliance and security issues. However, setting this up requires more effort and direct coordination with your cloud provider compared to using a SaaS LLM solution.

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1.3 On-Prem (Difficulty: Hard)

Deploying LLMs on-premises is a demanding endeavor. At Neural Bridge, we have successfully navigated this challenge and brought on-prem solutions to production. However, this pathway is significantly more complex due to the need for in-depth engineering and reliance on open-source LLMs or partnerships with specialized firms like Cohere or MosaicML. These models often fall short of their closed-source counterparts in aspects like language support and moderation capabilities, adding to the challenge.

 

Category 2: Language

Neural Bridge's journey in deploying LLMs spans three continents, encompassing a diverse range of languages, including non-Latin and Asian scripts. The language chosen for deployment can greatly impact the difficulty level. While deploying in English is straightforward, other widely spoken European languages present moderate challenges due to variations in model performance. Asian languages, with their distinct alphabets and linguistic structures, represent the most significant challenge, yet we have successfully deployed LLMs in these languages, showcasing our versatility and technical prowess.

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Category 3: User Type

The user type significantly impacts the complexity of LLM deployment, influenced by various factors like latency, reliability, safety, and cost. Each user type brings its unique set of requirements and challenges, shaping the approach and technology needed for effective implementation.

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3.1 Offline Jobs (Difficulty: Easy)

In scenarios where LLMs process large volumes of data offline on a daily basis, performance requirements such as latency and reliability are less critical. This use case is ideal for tasks like categorizing and storing vast datasets, where the immediacy of the LLM's response is not a priority.

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3.2 Internal Users (Difficulty: Medium)

Many enterprises employ LLMs to enhance internal operations, deploying tools like Enterprise Retrieval Augmented Generation systems or business automation platforms. While the performance expectations are higher than for offline jobs, there is greater leeway to manage and educate users about potential issues with latency and reliability. Examples include internal document management systems and automated customer support tools.

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3.3 External Users (Difficulty: Hard)

Deploying LLM applications for external users is the most challenging, demanding the highest levels of latency, reliability, and safety. Catering to a broad user base increases operational costs and necessitates a significant investment in technology and infrastructure.

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Category 4: Conversation Structure

The structure of the conversation with the LLM is a crucial aspect that dictates the level of complexity in deployment.

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4.1 Single Turn (Difficulty: Easy)

The simplest form involves a one-time interaction where a prompt is provided, and the LLM generates a single response. This format is straightforward and requires minimal management.

 

4.2 Single Turn with Guided Followup (Difficulty: Medium)

In some scenarios, users may have follow-up questions within a structured or guided framework. This approach is more complex than single-turn interactions but is more controlled than fully open-ended conversations.

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4.3 Conversational (Difficulty: Hard)

Engaging in open-ended, free-form conversations with LLMs is the most challenging. It requires extensive management in terms of contextual understanding, maintaining coherence over multiple turns, and ensuring safety and appropriateness of responses.

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Category 5: Content Type

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5.1 Text (Difficulty: Easy)

LLMs excel in processing text, as they are fundamentally designed for linguistic tasks. Their inherent strengths lie in understanding and generating textual content, making this the most straightforward application.

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5.2 Quantitative Data (Difficulty: Medium)

LLMs can handle unstructured text with ease, but they may struggle with complex mathematical operations. They are, however, adept at generating descriptive languages like SQL, which can be leveraged to analyze quantitative data.

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5.3 Image, Audio, Video (Difficulty: Hard)

While LLMs have shown impressive capabilities in handling non-textual content, developing production-quality applications in these areas remains challenging. At Neural Bridge, we have pioneered in this space but recognize the additional complexities involved.

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In conclusion, the process of productionizing LLMs is multifaceted, with varying degrees of difficulty across different categories such as deployment type, language, user type, conversation structure, and content type. Our experience at Neural Bridge across a variety of domains has exposed us to these challenges firsthand. Understanding these complexities is crucial for businesses aiming to leverage the full potential of LLMs in their operations.