NLP helps machines understand and process human language, while generative AI creates new content, including text, images and code. Although they overlap, traditional NLP can be more efficient for structured, high-volume tasks, while generative AI suits content creation and open-ended queries.

This guide explores their differences, business use cases, benefits and limitations, including insights from Bloomberg’s finance-focused language model. It also explains how UK and US businesses can choose the right approach based on their goals, budget and technical capabilities. Gen AI development services can help turn suitable use cases into practical business solutions.

Natural Language Processing Explained

Natural language processing (NLP) refers to the area of computer science and artificial intelligence concerned with comprehending, analysing and producing human language. It has a long history dating back to the 1950s, and has seen several stages of development:

Rule-based NLP: NLP with predefined grammar rules, dictionaries and patterns.
Statistical NLP: Analyses large amounts of text to identify patterns and make predictions.
Machine learning and deep learning: Using models such as word embeddings and recurrent neural networks to recognise patterns and understand text.
Transformer models: Models such as BERT use transformer architecture to understand context and relationships between words.
Large language models (LLMs): Models such as GPT, Claude, Gemini and Llama can understand and generate text, summarise information, answer questions and perform a range of language tasks.

Common NLP Techniques

NLP (Natural language processing) is used to enable the machine to process, comprehend and analyze human language. Some of the NLP techniques used are:

Text classification and intent detection: Classifies texts and detects the intention of the user.
Named entity recognition: This helps to identify the key information in the text, for instance, names, locations, time and money.
Sentiment analysis: This technique helps in detecting the opinion, attitude or emotion of the text.
Information extraction: This helps to extract valuable information from a document or conversation.
Search and information retrieval: This enables the search for relevant information in response to a query.
Machine translation: This translates text from one language to another language.
Speech recognition and transcription: This converts speech to text format.
Summarization and question answering: This performs a summary of the text and provides answers.

Generative AI Explained

The technology of AI generative models deals with creating information that has some patterns such as texts, emails, reports, coding, pictures/images, videos/audio, voice recordings, etc. The main difference between text generation AI and other models is that the former one has output texts by learning a high amount of data, while the others generate images, video, audio, and voice.

Because large language models are based on a high amount of text data, their main purpose is to predict the next word. However, due to the high amount of data, they still have the ability to answer questions, summarize, translate, write texts, solve problems, and perform other tasks even without specific training.

The Key Differences

Natural language processing NLP and generative AI are two different areas of research. While NLP has a narrow set of applications including sentiment analysis, classification, and information extraction, generative AI models can be used in multiple domains to accomplish diverse tasks, including question-answering. Beyond the technology, the decision also depends on the company’s priorities and budget.

Comparison factor
Traditional NLP
Generative AI (LLMs)

Main purpose
Understand, classify and extract information from text.
Generate content and respond to a wide range of prompts.

Typical output
Labels, scores, extracted data and search results.
Text, summaries, answers, code and other content.

Training approach
Often uses task-specific models trained on relevant data.
Typically starts with a pre-trained model that can be adapted through prompting or fine-tuning.

Flexibility
Usually designed for specific tasks.
Can handle multiple tasks with the same model.

Consistency
Often produces more predictable outputs for defined tasks.
Responses can vary and may contain inaccurate information.

Running costs
Often lower for high-volume, repetitive tasks.
Can be higher, depending on model size, usage and output length.

Processing speed
Often faster for straightforward tasks.
Can take longer, particularly for complex prompts and lengthy responses.

Explainability
Outputs and decision rules can be easier to inspect in some systems.
Individual responses can be harder to explain or trace.

Data requirements
Supervised tasks may require labelled training examples.
Pre-trained models can often perform tasks from instructions alone, although additional data may improve results.

Traditional NLP vs LLMs: When to Use Which

Traditional NLP and LLMs each have their applications based on the nature of the business needs. Traditional NLP is efficient for automating repetitive and routine processes to be performed with the highest precision and speed, while LLMs are utilized in complex, diverse, and information-intensive processes requiring human accuracy and common sense.

Business task
Better fit
Why it works

Classifying millions of emails or support tickets
Traditional NLP or a small fine-tuned model
Processes high volumes quickly and consistently, often at a lower cost.

Extracting fields from standard documents
Traditional NLP with OCR
Extracts predefined information that can be checked against expected formats.

Detecting spam and identifying fraud signals
Traditional NLP and machine learning
Analyses large volumes of data to identify patterns and flag suspicious activity.

Answering varied questions about business documents
Generative AI with retrieval (RAG)
Retrieves relevant information and uses it to answer open-ended questions.

Drafting customer replies, reports or summaries
Generative AI
Produces fluent, context-aware content that can be tailored to different needs.

Testing a new language-based use case
Generative AI
Lets teams test ideas through prompts before investing in task-specific training data.

Supporting decisions in highly regulated environments
Traditional NLP or task-specific models, with human review
Can make defined tasks easier to test and validate, although explainability and governance still depend on the system design.

Real Business Example: Bloomberg’s Language Models

The challenge:

Bloomberg has experimented with natural language processing for many years on its financial data products, from sentiment analysis and named entity recognition to news classification and question answering. With the advent of large language models, Bloomberg became interested in exploring if a model fine-tuned on financial language would have an advantage over more generic models at these financial tasks.

The solution:

In 2023, Bloomberg announced the creation of BloombergGPT, a language model with 50 billion parameters that was trained on a mixture of financial data from its archives and general-purpose datasets, representing hundreds of billions of tokens

Implementation:

The model was tested on standard financial NLP benchmarks, including sentiment and entity recognition, as well as for language tasks in general. Bloomberg’s team compared its performance to similar open models of comparable sizes.

Business outcome:

In the BloombergGPT research paper, the authors found that the model outperformed comparable open models on financial tasks while maintaining general performance; however, subsequent independent research suggested that much larger general purpose models such as GPT-4 may be competitive on many financial text tasks, showcasing general models’ rapid improvement.

The lesson for most businesses is clear: training your own large language model from scratch is rarely worthwhile. Using strong general models, grounded in your own data and combined with focused NLP for high-volume tasks, usually delivers more value at a fraction of the cost.

Generative AI Business Applications

Generative AI has significant potential in a business context to automate activities involving synthesis of information, answering queries, or interrogating large sets of data. Some common application domains include the following:

Knowledge assistants that answer queries based on relevant internal documents for employees.
Chatbots and response templates for customers.
Content creation, including marketing copy, product descriptions, and reports, with human editing.
Summarization of contracts, research, and meeting notes.
Code generation and review for software development.
Asking questions in natural language to perform analyses on data.

IIH Global’s generative AI services help organisations design and build these applications with appropriate safeguards.

NLP Use Cases That Remain Essential

Traditional NLP still has its place, particularly in domains where things need to happen quickly, consistently, and methodically. Typical business applications are:

Classification of email and tickets: Categorizes incoming emails and tickets into appropriate categories.
Extraction of entities: Extracts information from invoices and forms and stores them as records.
Sentiment and topic analysis: Analyzes customer feedback for sentiment and topic.
Search indexing and ranking: Makes it possible for users to find the right information immediately.
Monitoring for compliance: Identifies potentially non-compliant business correspondence.
Voice to text: Converts conversations into text.

All of these are likely to run in the background, processing enormous numbers of items quietly and efficiently, and best done by efficient, specialist models.

Risks and Governance Differences

Both approaches require governance, but the risks are different:

Traditional NLP has risks of biased training data, misclassification and drift over time. Testing is relatively simple as the outputs are fixed labels.
Generative AI introduces the risks of hallucination (confident but false statements), prompt injection, inconsistent outputs, intellectual property questions and potential leakage of sensitive data. Testing is more complex as the outputs are open-ended.

For generative AI, organisations should ground outputs in trusted data, require citations, ensure humans review the content and monitor the usage. UK GDPR applies to both if personal data is involved.

How to Combine NLP and Generative AI

Businesses don’t have to make a false choice between traditional NLP and generative AI. By using both technologies in tandem it becomes possible to balance among precision, adaptability and cost Here are five ways to use traditional NLP technology and generative AI models in the same product or service:

Classify first, generate second: Leverage NLP to classify incoming requests, and let generative AI handle the responses when needed.
Retrieve, then generate: Use search to find relevant documents, then let an LLM generate answers based on those results.
Extract and validate: Let generative AI parse through complicated documents, then apply rigorous validation rules to the extracted data.
Route by complexity: Use smaller, faster models to handle simple tasks, and route more complex requests to larger, more capable models.
Distil for scale: Take high-performing large language models and distill them down into smaller, more efficient variations that can handle higher request volumes.

Our natural language processing services design hybrid systems that use each technique where it adds most value.

Choosing the Right Approach

Deciding between the traditional NLP or the new generative AI is down to the task, desired outcomes, processing volumes and available resources. Use this four-step framework to determine which approach is more suitable for your organization.

What output do you need? Traditional NLP is often better for classifying text and extracting specific facts, while generative AI is better suited to tasks that require the creation of new content.
How many items will you process? If you have a high volume of repetitive tasks, specialised NLP models are likely to offer a better speed-to-cost ratio than generative AI.
How costly would an error be? For critical tasks, it is important to opt for a more proven output, thorough testing and human review.
Do you have labelled training data? If you don’t, generative AI can help you to test out an idea quickly by prompting, without first needing to prepare a labelled training set.

How Much Does NLP vs Generative AI Cost?

Traditional NLP projects can cost $5,000 – $30 000 for a basic or moderately complex solution, while generative AI applications may range from $10 000 – $100 000 or more, depending on the features, model integration and business requirements. Traditional NLP is often more cost-effective for high-volume tasks such as email classification and data extraction.

Generative AI typically requires a larger budget for applications such as knowledge assistants, document analysis and custom AI workflows. Ongoing costs for both approaches depend on usage, infrastructure and maintenance. These are indicative estimates, not fixed prices.

The Future of AI Language Models

The future development of artificial intelligence language models will lead to a convergence between traditional natural language processing and generative artificial intelligence. The focus will be on small models which will make AI more accessible for specific tasks, while AI agents will combine language understanding, content creation and automated execution of tasks. Firms will witness a rise in options to design solutions which incorporate efficiency, affordability and adaptability, but the effectiveness will be driven by the accuracy in picking the right technologies, addressing risks and measuring business benefits, not by embracing artificial intelligence on a wide scale.

Conclusion

While NLP and generative AI may seem like completely different approaches, enterprises don’t have to choose one over the other. Traditional NLP tools remain effective for managing high-volume, fast-moving data and handling tasks that require speed and consistency. Generative AI, meanwhile, is well suited to content creation, summarisation and answering open-ended questions.

The right approach depends on a company’s objectives, budget, technical capabilities, workload and business needs. Working with an experienced NLP services company can help businesses assess these requirements and identify the most suitable AI solutions.

Looking to identify the right AI approach for your business? Contact IIH Global to discuss your requirements and explore the right solution for your needs.

Frequently Asked Questions

What is the difference between NLP and generative AI?

NLP is the area of getting the computer to understand the nuances of language. Generative AI creates content. And large language models are generative AI models that are used for a wide variety of NLP tasks.

Is ChatGPT NLP or generative AI?

Both. ChatGPT is based on a large language model, which is a type of generative AI, and offers a range of natural language processing capabilities, such as summarising, translating and answering queries.

Is traditional NLP still relevant?

Yes, traditional and specialized NLP models are more effective than generative models for high-performance needs like classification, extraction, spam filtering, and search indexing.

Should my business build its own large language model?

Probably not because most of the time, you would want to use a strong model that leverages data from your domain via retrieval or fine-tuning rather than training a model from scratch.

Can NLP and generative AI work together?

Yes. Many systems are using NLP to classify, search, and extract, while generative artificial intelligence is utilized to respond to questions, summarize, and draft, thus balancing efficiency and flexibility.

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