Efficiency is key in complex organizations and enterprises, and the ability to generate documents quickly and accurately can make a difference. What if a solution could streamline this process, saving you time and resources while ensuring accuracy and compliance? 

With the help of Artificial Intelligence, companies are revolutionizing how they create documents.

AI text generation: challenges, advantages and limitations

The challenge of text generation lies in creating a system that can produce coherent, accurate, and contextually appropriate text. For instance, average AI tools might fabricate new text, disregarding your writing style, tone, enterprise type, and other specifics. This entails developing AI systems that can understand the nuances of language and generate content that seamlessly aligns with the user's requirements.

Additionally, Large Language Models (LLMs) can experience "hallucinations." Since they generate responses based on probabilistic predictions without accessing external knowledge sources, they are prone to errors when asked for specific content.

Once resolved, the benefits of AI text generation for document creation are numerous.

1. Efficiency:

By automating the document generation process, AI integration saves time and resources, resulting in efficiency.

2. Accuracy:

AI models can reduce the risk of human error by producing highly accurate and error-free documents.

3. Customization:

Users can input specific information and preferences, ensuring the generated documents meet their needs.

4. Scalability:

Whether you need to generate one document or a thousand, AI text generation can scale to meet your requirements effortlessly.

While AI text generation holds huge potential, it has limitations. For example, current systems may struggle to generate complete documents at once, often focusing on specific sections. However, ongoing advancements in AI technology are rapidly addressing these limitations, promising even greater capabilities ahead.

Real-world applications: harnessing AI text generation for success

One of the clients for whom we introduced AI for text generation is a legal firm in the need of handling various documents. These, ranging from contracts to legal opinions, can be time-consuming and complex to generate manually. By leveraging AI, the company now quickly generates customized documentation, tailored to its specific needs, saving time and resources while ensuring precision and legal compliance.

The partnership with this New York-based legal firm underscores the growing trend among law practices to embrace AI-driven solutions. These technologies empower legal professionals to focus more on strategic tasks and client interaction, while AI handles routine drafting tasks with speed and accuracy.

Leveraging Retrieval-Augmented Generation (RAG) enhances AI text generation by integrating a database of documents with a Large Language Model (LLM). When a query is made, RAG searches the database for relevant information and uses the LLM to generate a response. For instance, if a lawyer requests a confidentiality clause for an Agreement of Merger, RAG retrieves pertinent legal documents discussing confidentiality in the context of mergers and then the LLM generates a concise, accurate, and contextually enriched clause based on that information.

Text is generated using those documents and the user’s query or prompt, offering added benefits. Firstly, coherence is maintained throughout the generated text with already written content. Moreover, hallucinations (when GPT goes off track and replies with nonsense) are reduced.

The main advantage of text generation over ChatGPT is that while ChatGPT may provide generic and sometimes invented answers that do not align with your business style or tone, text generation tailors the response to the specific requirements and operational style of the company, ensuring that the generated text aligns with the firm’s legal standards and client needs, for example.

However, what seems straightforward has some delicate points. The search process is one of the main challenges in creating a RAG system. How can we efficiently search through thousands or millions of documents, each containing hundreds or thousands of pages, in a matter of a few seconds? This is where having the document summaries (and therefore clustering) becomes essential. When a query is received, first the theme is extracted. This theme is then matched with the document summaries, to find all documents that talk about this topic. To follow our initial example, we would get all documents related to merger agreements. This limits the search to summaries instead of searching through all documents, which would be impractical due to time and computational constraints.

Since the most relevant documents can be extensive, spanning thousands of pages, it's crucial to extract only the pertinent information. To address this, we identify the specific section relevant to the user’s query. For instance, in the scenario involving merger agreements, the system would extract the "Confidentiality clause" sections. This section is then matched within the document using a method similar to the one described earlier. Therefore, the context provided to the LLM consists of the most relevant sections from the most similar documents. In our example, this would involve multiple confidentiality clauses sections from documents discussing agreement of mergers.

The step by step of the process looks something like this: 

1. Query submission: a user submits a query, for example: "Draft a confidentiality clause for an agreement of merger."
2. Query parsing: the query is parsed to identify the main components:
   • Document Topic: "Agreement of merger"
   • Document Section: "Confidentiality clause"
3. Theme matching: the document topic is embedded and matched against a database of summary embeddings to identify relevant documents that discuss topics related to “agreement of merger”.
4. Section matching: sections relevant to the "Confidentiality clause" are extracted from the selected documents based on the document section specified in the user's query.
5. Tailored Generation: these extracted sections, along with the original user query, are provided to a language model like ChatGPT. This way a tailored section, specifically addressing the user's request is generated.

AI text generation's role in tomorrow's world

As AI technology continues to evolve, the possibilities for document generation are endless. Whether it's contracts, reports, or legal documents, AI text generation is poised to revolutionize how we work, making document creation faster, easier, and more efficient.

By collaborating closely with legal experts in New York, we are exploring how AI can transform traditional legal workflows, offering new opportunities for innovation and efficiency in the legal profession.

Besides visiting DocsHunter, our AI in-house product, feel free to check out our blog for deeper insights into the future of AI, machine learning, and their integration into today's technology landscape.