Large Language Models (LLMs) have become an indispensable tool in data processing and analysis. These models have revolutionised the way businesses handle information and make decisions. However, the most common approach to utilising LLMs - using public APIs such as OpenAI, Gemini or others presents significant challenges. In this article, we explore the drawbacks of conventional methods and introduce innovative, more efficient approaches such as LangChain and the Retrieval Augmented Generation (RAG) architecture. Additionally, we discuss practical use cases in the fields of recruiting and legal tech.

Traditional Approaches and Their Challenges

The traditional approach to utilising LLMs involves using a very large inference endpoint, fine-tuned with prompt engineering. This approach, often humorously referred to as "pray and hope," is inefficient in many ways. The main issues with this approach are:

• Cost: Deploying and maintaining large models is extremely expensive and poses a financial hurdle for many businesses.

• Latency: Processing large amounts of data results in significant delays, which negatively affects user experience.

• Privacy: Sending sensitive data to external models poses considerable privacy risks, especially in regulated industries.

Typical LLM Tasks

LLMs are versatile and are commonly used for the following tasks:

• Classification: Models are instructed to categorise inputs into predefined classes. This is particularly useful for processing large volumes of text data.

• Summarisation: Long texts are condensed into their essential content, making information processing easier.

• Entity Extraction: Models extract specific attributes from unstructured data, such as addresses from letters or contact details from CVs.

Programmatic Approach with LangChain

LangChain is a framework specifically designed for the efficient use of LLMs. It enables the linking of various components around LLMs, significantly increasing efficiency. The core concepts of LangChain include:

Prompt Templates: Predefined input patterns that simplify and standardise interactions with the model.

Agents: Tools that enable models to perform complex tasks, such as executing Python code, querying databases, and looking up information on the web.

Query Endpoints: Specialised endpoints that allow targeted data queries.

LLM Inference Endpoints: Endpoints tailored to the evaluation needs of the specific use case.

These components allow the composition of specialised LLMs for individual tasks, enhancing the system's efficiency and adaptability.

RAG Architecture: Separation of Model and Data

The Retrieval Augmented Generation (RAG) architecture offers an innovative solution to many traditional problems by separating the model from the relevant dataset. This is achieved in two steps:

Data Preparation: The dataset is split into smaller chunks using an embedding model and stored in a vector store. This step ensures that data is efficiently organised and easily retrievable.

Data Retrieval: Upon a query, the model extracts relevant data from the vector store to generate an accurate response. This allows targeted and efficient data processing without the model needing direct access to the entire dataset.

Use Cases of LLMs

Recruiting

LLMs offer significant advantages in the recruiting process and can be utilised in various ways:

CV Normalisation: LLMs can automatically process, normalise, and summarise incoming CVs. This simplifies the work of HR departments and ensures consistent data.

Role Matching: LLMs can efficiently filter and evaluate applicant profiles, reducing the workload of talent teams and enabling faster and more accurate selection of the best candidates.

Legal Tech

In the legal tech sector, LLMs offer numerous applications:

Enhanced Data Rooms: LLMs enable conversational search and discovery in complex negotiations, such as corporate mergers. This facilitates access to relevant information and accelerates the negotiation process.

Amplified Paralegal: LLMs assist with case analysis and summarisation, and research of precedents and commentaries. This alleviates the workload of legal professionals and increases efficiency.

Legal Text Copilot: LLMs can support the creation and editing of legal documents by providing precise and relevant text suggestions.

Conclusion

A programmatic approach to utilising LLMs, as enabled by LangChain and the RAG architecture, offers an efficient and cost-effective alternative to traditional methods. By separating the model from the data and using specialised components, businesses can fully leverage the power of LLMs while minimising costs, latency, and privacy issues.

These new approaches and technologies are not only innovative but also practical and applicable across various industries. They provide businesses with the tools to optimise their data processing and analysis, improving efficiency and accuracy. In a world increasingly dominated by data, such solutions are essential for remaining competitive and fully exploiting the available information.