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Enterprise Generative AI Tools for Scaling LLM Development in Your Enterprise

Due to their non-deterministic nature, transferring LLMs from prototype to production presents cost, performance, and evaluation challenges. Organizations must use enterprise generative AI tools that support LLM development and deployment to succeed. 

LLM application development typically involves LLM selection, customization, testing, and monitoring. This article looks at top enterprise generative AI tools for each step so you can build and run more AI applications throughout your organization.

Summary of key enterprise generative AI tools

Tool name Purpose Key features
Language Model Evaluation Harness Testing and assessing LLMs
  • Includes several academic benchmarks with additional subtasks.
  • Supports various LLMs
  • Interoperates with commercial APIs.
PromptFlow Prompt engineering and LLM customization Allows users to integrate LLMs, prompts, Python functions, and conditional logic to create flowcharts.
Llama Factory Comprehensive toolbox for fine-tuning over 100 different LLM models.
  • Makes the fine-tuning process accessible to both technical and non-technical users. 
  • Offers a unified interface for various LLMs and applications.
Unsloth Optimizing the fine-tuning pipeline. Decreases LLM training time while reducing memory usage.
No need for hardware changes during optimization.
Nexla Data integration from any source to any vector database.  No-code integration with vector databases for the automatic retrieval of relevant data in RAG workflows.
LangChain Framework to build with LLMs by chaining interoperable components. Provides abstractions for faster coding of generative AI applications.
Giskard Detect performance, bias, and security issues in AI models.
  • Assess the correctness of responses generated by RAG models.
  • Identify problems such as hallucinations, harmful content, prompt injection vulnerabilities, etc.
LangSmith A complete platform for profiling, debugging, and benchmarking LLM applications. An all-in-one developer platform for debugging, testing, and monitoring LLM applications.
Evidently An open-source Python library for evaluating, testing, and monitoring LLMs. 
  • Automates checks of LLM output properties.
  • Provides visual reports and graphs for result evaluation.
  • Real-time monitoring dashboards with custom panels and alerts,

The rest of this article looks at different stages of enterprise generative AI development and how the tools outlined above support each stage.

LLM selection

Choosing a large language model involves considering several factors, such as cost, complexity, and use case. For example, Chatbots with low daily requests may require one type of LLM, while tasks that require handling complex technical documents require another. Smaller LLMs with fewer parameters are better suited for edge applications with limited resources. Large LLMs provide a more nuanced understanding of the language but require considerable computational power. Open-source LLMs need resources to get set up and then deployed on AWS/Google Cloud, etc. Enterprise LLMs can be set up much more easily but have less flexibility in deployment choices and come at a price.

Best practices in LLM selection

Given the options, it is a good idea to begin with more intelligent models, which help tailor the prompts better, and later review smaller models to reduce cost without sacrificing quality. Alternatively, you can consider the cascade method—begin with the smallest model version for every given request and then scale to larger models in sequence if the smaller model produces suboptimal responses. That way, you can balance between costs and performance.

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The Hugging Face leaderboard is a good way to compare various LLMs. Some of the main benchmarks used include:

  • Arena Elo evaluates the relative skill levels of various LLMs based on their performance in anonymous, randomized battles. 
  • AI2 Reasoning Challenge (ARC) measures a system’s ability to answer questions that require complex reasoning over text.
  • HellaSwag assesses common sense reasoning in natural language generating and understanding.
  • Multitask Multidomain Language Understanding (MMLU) evaluates knowledge across 57 topics to determine how well the model can learn in zero-shot and few-shot settings.
  • TruthfulQA evaluates whether the model provides true responses and if the model intentionally gives inaccurate data.
  • MT-Bench measures both conversational ability and compliance with instructions in multi-turn dialogues.

The Language Model Evaluation Harness by Eleuther AI is the backend of the Leaderboard and can be used directly as well. It is a comprehensive framework for testing and assessing LLMs. It offers approximately 60 typical academic benchmarks, supplemented by hundreds of subtasks and permutations. The platform supports a variety of LLMs, including those loaded via the transformers library, GPT-NeoX, and Megatron-DeepSpeed. It is also interoperable with commercial APIs such as OpenAI and TextSynth and can test adapters such as LoRA using HuggingFace’s PEFT library. 

The system supports standardized evaluation using publicly available prompts for reproducibility and comparability. You can also provide custom prompts and evaluation criteria. You get a flexible, token-agnostic interface and memory-efficient inference with vLLM.

This program has been used in hundreds of scientific publications and is utilized internally by various enterprises, including NVIDIA, Cohere, BigScience, BigCode, Nous Research, and Mosaic ML.

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LLM customization 

Once you have selected your model, it is time to customize it. The goal of customization is to make sure the model responds to prompts that your customers expect. For example, if you want your chatbot to answer questions about your organization’s HR policies, you need to make sure it has access to your internal policy documents.

There are several different approaches to LLM customization.

Prompt engineering

Prompts are detailed instructions or questions on which the answer of an LLM is based. Prompt engineering is crafting inputs that steer the model toward desired solutions. There are many different strategies—for example, 

  • Zero-shot prompting—the model answers a new query without any examples of the same type seen before.
  • Few-shot prompting—the model receives a few examples of the task at hand before generating the response.
  • Self-reflection—each trained model monitors its own feedback to try and catch any errors it may be making the best it can.

OpenPrompt is a library built upon PyTorch that provides a standard, flexible, and extensible framework for prompt engineering. PromptFlow is another free, open-source, low-code tool that allows users to integrate LLMs, prompts, Python functions, and conditional logic to create flowcharts.

Fine-tuning

Fine-tuning involves retraining the main model’s weights to adapt to certain tasks. It turns a general large language model into a domain-sensitive one for better results. For example, where the task is very specialized, like converting a question into an SQL query. 

A good fine-tuning approach is using a very powerful model to generate sample data. For example, GPT-4 or Claude 3 can create a dataset of specific questions and answers. This data can then be used to train the model on specific tasks, such as transforming a question into an SQL query. ​​However, keep in mind that fine-tuning typically requires significant resources.

Llama Factory is an open-source framework that simplifies and speeds up the process of fine-tuning complex language models. It is a complete toolbox that allows you to fine-tune over 100 distinct LLM models, including LLaMA, BLOOM, Mistral, Baichuan, Qwen, and ChatGLM. It offers a common interface for easily fine-tuning a variety of LLMs for different use cases and domains.

Unsloth is another open-source software that can assist you in fine-tuning your model. It decreases LLM training time while reducing memory usage. There is no need for hardware changes during optimization.

Here is an example of how you could use it.
First, install it as below.

!pip install "unsloth[colab] @ git+https://github.com/unslothai/unsloth.git" -q
!pip install "git+https://github.com/huggingface/transformers.git" -q

Then run the following code.

from unsloth import FastLanguageModel
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="unsloth/mistral-7b-bnb-4bit",
    max_seq_length=2048,
    load_in_4bit=True,
)
trainer = Trainer(model=model, tokenizer=tokenizer, ...)
trainer.train()
FastLanguageModel.for_inference(model)
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer=text_streamer, max_new_tokens=64)

Retrieval-augmented generation 

RAG uses a language model for response generation and an information retrieval system for context supplementation. When a user asks a question, the RAG system:

  • Represents the query as a vector.
  • Performs a similarity search within a vector database to query the relevant data.
  • Integrates the search results with the user’s query and provides them as context to the language model.
  • Generates a response based on both the original query and the information retrieved.

RAG allows for an updated context with dynamically integrated and relevant information. This is ideal for providing the freshest possible knowledge for questions or context related to recent events. RAG can also leverage sophisticated methodologies such as query re-ranking to reprioritize the original search results based on the assumed question intent.

Nexla is a data integration platform that lets you build retrieval-augmented generation workflows without code or structure. You can automate workflows from any data source to any vector database, such as Pinecone, Weaviate, or Redis. Nexla makes it easy to deliver data back to the model for improvement. See an example tutorial of how you can speed up GenAI development by creating dataflows in Nexla. 

Screenshot showing mock dataflow from Snowflake to Pinecone using Nexla

LangChain is another open-source framework that lets you build RAG pipelines—but coding skills are a must. See the snippet below.

from langchain_core.runnables import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough 
message = """ 
Answer this question using the provided context only. 
{question} 
Context: {context} 
""" 
prompt = ChatPromptTemplate.from_messages([("human", message)]) 
rag_chain = {"context": retriever, "question": RunnablePassthrough()} | prompt | llm

LLM testing

LLM testing includes traditional software testing practices as well as specific evaluations to ensure the quality of LLM responses. Utilize a more advanced LLM to evaluate responses generated by a less powerful model on a predefined scale. Using predefined datasets with known questions and answers allows for a direct comparison of the model’s responses.
Giskard is an enterprise generative AI tool for setting up and running tests on your AI applications. You can automate testing & compliance across your GenAI projects.

LLM monitoring

Real-time monitoring is crucial to ensuring LLMs function correctly and respond appropriately. It allows you to observe model behavior in real time, quickly identify issues, and continuously improve performance.

LangSmith is a platform for profiling, debugging, and benchmarking LLM applications. It makes the model run trackable by recording what input values were used to generate outputs and all steps used to produce the result. You get a detailed view of the internal reasoning of your LLM for troubleshooting. You can also make model responses available to collect human feedback while running beta tests. 

Screenshot of Langsmith demo

Evidently AI is an open-source Python library to evaluate, test, and monitor your LLMs. You can perform task-specific evaluations for customer support chatbots or creative writing assistants. It is important because the performance metrics for different applications can vary greatly. Additionally, you can use this enterprise generative AI tool to automate checks of LLM output properties like text length, readability, and tone.

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Conclusion

The techscape of large language models is evolving at breakneck speed. To remain cutting-edge, you must be aware of new developments and implement flexible systems that can be changed as AI advances. Investing in reliable AI infrastructure and automation is the key to success. High-quality enterprise generative AI tools can accelerate AI development.

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