Specialised AI vs General AI

Generative AI is being used across a growing number of industries to improve decision-making, reduce costs, and automate all manner of knowledge-based tasks. But not all AI is created equal. Two distinct types – general AI and specialised AI – offer very different capabilities. Before investing in one or the other, or both, understanding their strengths and limitations is crucial to making smart business decisions.

 

General AI is good for a huge range of tasks, from analysis, reasoning and general knowledge Q&A to even full content creation. The models that power these AIs are trained to handle practically any use case. Famous models such as OpenAI’s GPT series, Google’s Gemini, and X’s Grok are well-known examples of such models. However, as with humans, the saying can go “jack of all trades, master of none”, so when it comes to more specialist use cases, such as document data extraction, these models may be able to do a decent job, but do not reach the quality and accuracy that users need.

 

Specialised AI models can bridge the gap. These models are tuned and trained with a biased knowledge towards the use case they have been built for, with an enhanced ability to handle certain tasks. Thus, while they may not perform well on general tasks, they excel at their specialisation. For example, Xtracta is built for accuracy within specific tasks and workflows. Today, let’s explore what each brings to the table and why specialised solutions are the better choice for business applications such as contract processing.

The Backend: What is Generative AI?

Practically all modern-day AI, both General and Specialised, are based on a technology called Generative AI (GenAI), which is a type of artificial intelligence model designed to create content by recognising and replicating patterns learned from large datasets. Popularised by chatbots like ChatGPT, GenAI can produce a wide range of content, including text, images, code, and more. These models are typically built on large language models (LLMs), which generate responses based on user prompts.

 

When prompted, GenAI predicts and generates the most likely response based on its training data. Well-known examples include models trained and built by companies such as Claude and Perplexity, with new models frequently emerging, such as Grok on X (formerly Twitter).

 

Strengths and Limitations of Generative AI

GenAI Strengths: Versatile and Accessible

At first glance, GenAI is highly versatile. People use it to write emails, generate marketing assets, or even simulate conversations. Its flexibility makes it attractive for those who want a tool that does a little bit of everything. Its chatbot design is user-friendly and intuitive, making it ideal for anyone who doesn’t consider themselves tech-literate to pick up with ease. Its accessibility has seen it widely adopted, especially by organisations looking to scale content production.

 

For visual outputs, GenAI has also lowered the barrier to entry for tasks that previously required training or skills. Social media teams, for example, can generate draft visuals to present to designers.

 

GenAI Limitations: Hallucinations and Generic Results

GenAI comes with trade-offs. Although often used for a wide scope of tasks, GenAI has a tendency to produce incorrect, incomplete, or misleading information – often with confidence. These are known as “hallucinations”. Since it relies on probability rather than reasoning or true understanding, accuracy remains one of GenAI’s most significant limitations.

 

GenAI-based general models – the most prevalent and widely known GenAI technology currently available also lack specific domain awareness. A generative model trained on general web data won’t reliably interpret industry-specific language, legal clauses, or compliance obligations in fields where detail and context matter; this can lead to errors or missed risks.

 

Although many who promote the technology emphasise the importance of fine-tuning prompts (within general models) to achieve the result users want, the randomised nature of these models means replicating results can also be hit or miss, and the likelihood of incorrect information is high. It can also quickly become unmanageable and expensive if too much fine-tuning of prompts is needed to optimise for a wide range of use to get the desired accuracy.

 

What is Specialised AI?

Specialised AI refers to AI models built for a specific task or process. These models are trained on quality datasets based on the type of work they will be used for. For example, Xtracta—an AI tool that extracts and interprets data from documents— trains its models on documents like invoices, receipts, contracts, and similar materials that reflect its customers’ use cases.

 

Xtracta is trained to identify the specific data users need, allowing it to process documents with errors or varying formats without requiring additional fine-tuning or prompting. Whether it’s extracting key dates from legal agreements or spotting anomalies in supplier invoices, specialised AI is designed to recognise what matters within a specific context. Unlike general-purpose models, specialised AI is built for flexibility within a defined domain—purpose-built for a task or process rather than applicable across all areas of a business.

 

Additionally, and importantly, specialised models offer the opportunity for additional capabilities to be added that are relevant or even critical for the use case of these models.

 

Specialised AI Strengths: Accuracy and Consistency

The primary benefit of specialised AI is precision. Because these models are typically designed for one purpose, they can achieve a high degree of accuracy that general models struggle to match. For example, a specialised model trained on commercial lease agreements will recognise critical terms and obligations far more reliably than a general model. In the same vein, using general AI to process invoices is likely to result in errors and hallucinations. When dealing with hundreds of invoices daily, this isn’t good enough.

 

For use cases like document data extraction, additional capabilities such as high-accuracy grounding can assist in the process. Grounding is AI industry parlance for when the model has to prove how it arrived at a value by providing evidence within the input. In the case of document data extraction, this is done by showing exactly where on a document a value has come from and proving to the user that it actually exists. Most general models currently do not have this capability, while specialist models can be optimised and trained to do this.

 

Specialised AI is also often designed for seamless integration with the software a business already uses. Robust compatibility means less time spent setting up an AI tool at the start. For example, rather than starting with a blank canvas and open prompt as you would with a general model, Xtracta’s models are all pre-designed with exactly what users need, from pre-designed (and optimised) prompting to prompt library management, structured data outputs, audit logs, etc.

 

Limitations

Although specialised AI is not designed to be used outside of its established scope, one limitation of specialised AI on paper is that it cannot be used for as many things as generative AI can. A specialised model trained for legal document review won’t also work well for fraud detection of accounting documents without being given this type of data to retrain with.

 

General AI vs Specialised AI for Contract Processing

Contracts are among a business’s most important documents. They define obligations, rights, risks, and timelines, yet many businesses still process them manually or, for some, not at all. Whether reviewing clauses before renewal or extracting key terms for compliance, contract processing demands a level of accuracy and contextual understanding that general-purpose models struggle to deliver.

 

General AI could be used to summarise contracts or identify key phrases, but it lacks the accuracy needed to do so reliably. These models generate outputs based on likelihood rather than certainty. As a result, they can misinterpret critical information. Specialised AI-powered software, however, is designed specifically to handle complex documents like contracts. These models have particular knowledge about how contracts are constructed and their content, improving the way they interpret and capture the data requested.

 

Xtracta can automate your contract process by:

 

• Searching for provisions or issues and instantly retrieving relevant clauses and sub-clauses.
• Interpreting and matching content to specific legal conclusions such as termination triggers, obligations, or renewal conditions.
• Comparing contracts and identifying differences in employment terms or lease conditions across multiple documents.
• …and more!

 

Choose an AI Model that Works for You with Xtracta

While general AI (GenAI) often dominates the conversation around AI, it doesn’t necessarily represent what’s optimal for businesses. General AI can be useful for brainstorming, drafting, outlining and a diverse range of other tasks. However, its use case offers diminishing returns at best and unhelpful information at worst when extended to tasks requiring precision. For businesses looking to gain a competitive advantage through AI, general models don’t have the accuracy, accountability, or risk management needed when dealing with high-stakes work.

 

For organisations working with contracts, invoices, and other types of important documents in high volumes, relying on general-purpose models can result in inaccuracies. Specialised AI models deliver focused results to support better business decisions. Xtracta is designed for specific tasks that address industry-specific pain points.

 

To learn how Xtracta’s trained, specialised AI can help your business, talk to one of our experts today.