NLP Project Ideas with Source Code: Text to LLMs

University NLP projects are already working with the tools behind today’s language applications. Stanford’s CS224N course still covers the basics of NLP. Still, its 2026 coursework also includes BERT, pretraining, LoRA, and other parameter-efficient fine-tuning methods, prompting, RAG, tool use, and language-model evaluation. Students finish the course with a research project where they test their approach and report the results.

For a student building nlp projects, there is a lot you can learn before getting into LLMs. Text classification, sentiment analysis, NER, and search can teach you how text is cleaned, represented, compared, and classified. Later projects can add embeddings and transformers, followed by document retrieval, RAG, structured extraction, and LLM evaluation.

The 18 projects in this article cover these nlp projects with source code, starting with classical nlp project ideas and ending with LLM applications. You’ll also find the datasets, tools, and metrics used for the different projects.

NLP in 2026: Is It Still Useful

If you’re searching for nlp projects in 2026, you may wonder whether techniques such as TF-IDF, stemming, and Naive Bayes are still worth learning when transformers and LLMs are now so common.

And yes, they are still part of the technical picture. A TF-IDF classifier, for eg, can give you a baseline for a text-classification problem before you try a larger model. It can also be much cheaper to run when the task only requires a fixed prediction. An API call to GPT-5.6 Luna currently costs $0.20 per million input tokens and $1.20 per million output tokens, so the difference becomes important when an application processes a large amount of text.

You may also encounter these concepts when applying for NLP roles. TF-IDF, tokenisation, Naive Bayes, embeddings, and evaluation metrics such as Precision, Recall, and F1-score are still common parts of NLP interviews. Knowing them also makes it easier to understand why a particular model was chosen instead of simply reaching for an LLM.

This is why a 2026 nlp projects portfolio does not need to discard older methods. A project using a traditional classifier can show how you approach a problem, establish a baseline, and evaluate a model just as a transformer or LLM project can demonstrate newer skills.

If you’re not sure what to learn after these projects, this NLP Roadmap can help you plan your next steps.

Era 1: Classical NLP Baselines (Projects 1-6)

Classical NLP uses statistical methods and text features to work with language. Techniques such as TF-IDF, Naive Bayes, and NER can be used to classify text, find important information, or retrieve relevant documents without relying on a large language model. These methods are still useful when a task needs a fast, lightweight model or when you want to understand exactly how the system reaches its output.

The six nlp project ideas in this section cover TF-IDF, text classification, sentiment analysis, keyword extraction, Named Entity Recognition (NER), and information retrieval. For the implementation, you’ll work with libraries such as scikit-learn and spaCy, using them for different parts of the NLP workflow. The projects take you through classification, sentiment analysis, information extraction, and search before you move on to transformer models and LLM applications.

1. Spam Email Classifier using TF-IDF and Scikit-learn

Email providers process millions of messages every day, making spam detection one of the earliest and most practical NLP applications. In this text classification project, train a machine-learning model to distinguish spam from legitimate emails. Start with a labelled email dataset, clean the text, and use TF-IDF to turn each email into numerical features. With TfidfVectorizer from scikit-learn, train a Multinomial Naive Bayes or Logistic Regression classifier on these features.

Test the trained model on emails it has not seen before and check accuracy, precision, recall, and F1-score. Precision is particularly important because a false positive could send a legitimate email to the spam folder.

What you’ll need: Python, pandas, scikit-learn, and a labelled spam-email dataset.

Source code/tutorial: Text Classification Using Scikit-learn

Skills you’ll build: TF-IDF, text preprocessing, feature extraction, Naive Bayes, Logistic Regression, text classification, model evaluation.

2. Twitter sentiment analysis project

Social media gives businesses instant feedback on product launches, marketing campaigns, and customer experiences, but going through thousands of posts manually isn’t quite practical. Sentiment analysis can sort these posts into categories such as positive, negative, or neutral.

Use a labelled collection of tweets and train a classifier to predict the sentiment of new posts. Tweets are short and informal, so clean the text carefully. Handle hashtags, emojis, mentions, links, abbreviations, and repeated characters without removing useful information. You can then represent the tweets with TF-IDF or word embeddings and train a classification model.

Compare different preprocessing choices and check precision, recall, and F1-score for each sentiment class. This will especially come of use when one sentiment appears much more often than the others.

What you’ll need: Python, pandas, scikit-learn, a labelled Twitter sentiment dataset, and a text-preprocessing setup.

Source code/tutorial: Social Media Sentiment Analysis

Skills you’ll build: Text preprocessing, sentiment classification, noisy-text handling, TF-IDF, word embeddings, model evaluation.

3. Named Entity Recognition (NER) Pipeline with spaCy

Named Entity Recognition helps extract specific details from unstructured text. In this project, use spaCy to find entities such as people, organisations, locations, dates, and monetary values in documents. Start with spaCy’s pre-trained NER pipeline and run it on sample documents to see which entities it identifies correctly and where it makes mistakes.

For a more advanced version, train the pipeline on your own labelled examples. This works particularly well with domain-specific documents, where the standard model may not recognise the terms and entities you need. Recruitment resumes, financial reports, and legal documents are good examples.

What you’ll need: Python, spaCy, a collection of sample documents, and labelled examples if you plan to train a custom NER model.

Source code/tutorial: Named Entity Recognition

Skills you’ll build: Named Entity Recognition, spaCy, entity extraction, text preprocessing, custom NER training.

4. Keyword Extraction Tool

Reading a long article or report can make it hard to identify what the text is mainly about. Keyword extraction can pull out the important terms and phrases, which can then be used to organise documents, improve search, or generate a quick overview of the content.

For this project, start with TF-IDF to rank terms based on how important they are in one document compared with the rest of the collection. Then try YAKE! This is an unsupervised keyword-extraction method that looks at features such as word frequency, position, and context within a single document. Run both methods on news articles, blog posts, or research papers and compare the keywords they produce.

What you’ll need: Python, a collection of documents, scikit-learn for TF-IDF, and the YAKE! Python library.

Source code/tutorial: YAKE! – GitHub

Skills you’ll build: Keyword extraction, TF-IDF, YAKE!, text ranking, document analysis.

5. News Search Engine

When you search for a news topic, you expect the most relevant articles to appear first. A basic search engine has to take the words in your query, compare them with a collection of articles, and decide which documents deserve the highest rank.

For this nlp project, use the BBC News Classification Dataset and preprocess the articles with steps such as tokenization, stop-word removal, and stemming. Use BM25 with the rank_bm25 library to score the articles against a user’s query and return the highest-ranking results. BM25 is related to TF-IDF, but it also accounts for factors such as term frequency and document length when calculating relevance.

Try different search queries and check whether the highest-ranked articles are actually relevant. You can also experiment with the preprocessing steps and see how they affect the results.

What you’ll need: Python, pandas, NLTK, Gensim, rank_bm25, and the BBC News Classification Dataset.

Source code/tutorial: Build a News Search Engine with NLP

Skills you’ll build: Information retrieval, text preprocessing, BM25, document ranking, query matching, search pipelines.

6. Resume Parser

Resumes come in different formats and use different headings for the same information. One may have a “Technical Skills” section, another may use “Core Competencies,” while education and work experience can be arranged in completely different ways. A resume parser needs to find these details and turn them into consistent fields such as name, skills, education, job titles, and experience.

Here, extract text from resumes and use spaCy’s Named Entity Recognition (NER) to identify the information you need. Start with a pre-trained NER model and train it further with labelled resume data for fields that aren’t recognised correctly. You can then store the extracted details in a structured format for candidate search or skill matching.

What you’ll need: Python, spaCy, resume documents, and labelled resume data for custom entities.

Explore the source code: GitHub Resume Parser Projects 

Skills you’ll build: Resume parsing, Named Entity Recognition, information extraction, custom NER training, structured data extraction.

Era 2: Embeddings & Transformers (Projects 7-12)

Imagine searching for “automobile” and missing a relevant document simply because it used the word “car” instead. Classical NLP often relied on exact word matches or handcrafted features, which sometimes made it difficult to capture the meaning behind text when another word would be used instead.

Embeddings and transformer models made it easier for NLP systems to work with meaning and context. Embeddings help a model recognise that words used in similar situations can be related, even when the words themselves are different. Transformers go a step further by looking at the words around a term, which helps the model understand what that term means in a particular sentence. These techniques are now used for tasks such as sentiment analysis, summarization, question answering, and semantic search.

The nlp project ideas in this section cover word embeddings, BERT fine-tuning, text summarization, question answering, emotion detection, and toxic-comment classification. You’ll use pretrained models from the Hugging Face ecosystem for the transformer-based projects. 

If you’re new to deep learning, Scaler’s free Keras & TensorFlow course covers neural networks, transfer learning, and transformer architectures before you start.

7. Word Embedding Explorer

As we mentioned previously, consider these words, “car” and “automobile.” A traditional text classifier may treat them as two separate words, but word embeddings can represent them as vectors that reflect how they are used in language. Words that appear in similar contexts tend to have similar positions in this vector space.

For this, use a pretrained Word2Vec, GloVe, or FastText model and create an explorer where you can enter a word, find its closest terms, and see how related words are grouped together. Try word-analogy tasks such as “king” − “man” + “woman” and visualise the embeddings in two or three dimensions using PCA or t-SNE.

What you’ll need: Python, a pretrained Word2Vec, GloVe, or FastText model, and a library such as Gensim for working with word embeddings.

Source code/tutorial: Word Embeddings with TensorFlow

8. Fine-Tune BERT for Sentiment Analysis

A pretrained BERT model already contains language patterns learned from large amounts of text. In this project, adapt BERT to sentiment analysis using the IMDB movie-review dataset, where reviews are labelled as positive or negative. Fine-tuning allows the model to adjust its existing language representations to recognise these sentiment labels.

Use the TensorFlow tutorial to load the dataset, preprocess the text for BERT, add a classification layer, and fine-tune the model on the labelled reviews. Once trained, test it on unseen reviews and compare its predictions with the TF-IDF-based sentiment classifier from Project 2 using accuracy, precision, recall, and F1-score.

What you’ll need: Python, TensorFlow, TensorFlow Hub, TensorFlow Text, the IMDB dataset, and a pretrained BERT model.

Source code/tutorial: Classify Text with BERT – TensorFlow

Skills you’ll build: BERT fine-tuning, transfer learning, text preprocessing, sentiment classification, model evaluation.

9. AI-Powered Text Summarizer

Newsrooms, legal teams, and analysts often have to work through large amounts of text before they can get to the information they need. A summarizer can shorten that material while retaining the main points.

One way to build this is with T5, a sequence-to-sequence transformer that generates a summary from the source text. The Hugging Face tutorial uses the BillSum dataset, which pairs legislative documents with human-written summaries. Train T5 on these text-summary pairs, then use the model to generate summaries for documents it has not seen before. ROUGE scores can help compare the generated summaries with the reference summaries.

What you’ll need: Python, Hugging Face Transformers, Datasets and Evaluate, the BillSum dataset, and a pretrained T5 model.

Source code/tutorial: Summarization with Transformers – Hugging Face

Skills you’ll build: Text summarization, sequence-to-sequence models, T5, transformer fine-tuning, text generation, ROUGE evaluation.

10. Context-Aware Question Answering System

A question-answering system works with a passage and a question, then identifies the part of the passage that contains the answer. For example, given a product manual and the question “How long does the battery last?”, the model should locate the relevant words in the manual instead of generating an answer from scratch.

Use DistilBERT and the SQuAD dataset to train an extractive question-answering model. The dataset provides a question, its context, and the exact answer span within that context. During preprocessing, you’ll also need to handle longer passages that exceed the model’s input limit. After fine-tuning, test the model with new questions and check whether it identifies the correct answer span.

What you’ll need: Python, Hugging Face Transformers, Datasets and Evaluate, the SQuAD dataset, and a pretrained DistilBERT model.

Source code/tutorial: Question Answering with Transformers – Hugging Face

Skills you’ll build: Extractive question answering, DistilBERT fine-tuning, tokenization, context handling, answer extraction, model evaluation.

11. Emotion Detection from Text

“Positive” and “negative” as answers are not enough sometimes. A customer review can show frustration, relief, excitement, or disappointment even when the overall sentiment appears similar. An emotion detection model looks at the text more closely and assigns it to specific emotion categories.

For this project, train a transformer model to recognise multiple emotions from labelled text. GoEmotions is one dataset you can use for this, with 27 emotion categories and a neutral label. Some examples carry more than one emotion, so the project introduces multi-label classification alongside transformer-based text classification.

Fine-tune a model such as BERT or RoBERTa, then test it on new text and check precision, recall, and F1-score for the different emotion categories.

What you’ll need: Python, Hugging Face Transformers, a labelled emotion dataset such as GoEmotions, and a pretrained transformer model.

Source code/data: GoEmotions Dataset – Hugging Face

Skills you’ll build: Emotion classification, multi-label classification, transformer fine-tuning, Hugging Face Transformers, model evaluation.

12. Toxic Comment Detection

Online platforms receive far more comments than people can review manually. A moderation system can flag comments that contain toxic, abusive, or threatening language so they can be reviewed or handled automatically. The difficult part is that the model has to deal with different types of harmful content, and getting a prediction wrong can affect the person who wrote the comment or the person reading it.

You can use the Jigsaw Toxic Comment dataset, which contains Wikipedia comments labelled for categories such as toxic, severe toxic, obscene, threat, insult, and identity hate. Train a transformer-based text classifier on these labels and test it on comments the model has not seen before. Since the categories are not mutually exclusive, this is a multi-label classification problem. Evaluate each label with precision, recall, and F1-score instead of relying on overall accuracy.

What you’ll need: Python, Hugging Face Transformers, the Jigsaw Toxic Comment dataset, and a pretrained transformer model.

Source code/data: Jigsaw Toxic Comment Dataset – Hugging Face

Skills you’ll build: Toxicity detection, multi-label classification, transformer fine-tuning, content moderation, model evaluation.

Era 3: LLM Applications (Projects 13-18)

What happens when an LLM needs information that isn’t part of its training data? It can still produce an answer, but that answer may not be based on the information you actually wanted it to use. This is where techniques such as semantic search and Retrieval-Augmented Generation (RAG) become useful. They allow an application to find relevant information and provide it to the model before it generates a response.

LLM applications also need to handle tasks that go beyond answering questions. They may have to summarise information from several sources, extract specific details into a fixed format, measure the quality of their responses, or prevent certain outputs. The llm nlp projects in this section cover semantic search, RAG, multi-document summarization, structured extraction, prompt evaluation, and guardrails, giving you a look at the different pieces involved in building LLM-based applications.

13. Semantic Search over Documents

A search for “vacation rules” may miss a document titled “annual leave policy” if the system only looks for matching words. Semantic search can connect the two by looking at the meaning of the query and the text being searched.

For this nlp project, create a collection of documents in Elasticsearch and use its semantic_text field to generate and store semantic representations of the content. Convert the user’s query into the same semantic space and retrieve documents based on their similarity to the query. You can also combine semantic search with traditional keyword matching to see how hybrid search affects the results.

Try queries that use different wording from the documents and check whether the relevant results still appear near the top.

What you’ll need: Python or Elasticsearch’s query tools, an Elasticsearch instance, a collection of documents, and a model for generating embeddings.

Source code/tutorial: Get Started with Semantic Search – Elastic

Skills you’ll build: Text embeddings, semantic search, vector search, Elasticsearch, similarity retrieval, hybrid search.

14. Build a RAG Chatbot Using Your Own Documents

What if you want a chatbot to answer questions using information from your own documents? A regular LLM may not have access to that information, even if it can produce a convincing response. A RAG system handles this by finding relevant content first and giving it to the model as context.

You can start by learning how AI chatbots work with this guide to chatbots in AI, then build a RAG chatbot that works with your own documents. The documents are indexed so the system can search them when a question comes in. The relevant sections are then passed to the LLM, which uses them to generate the response. You can also include citations so users can see where the answer came from.

Microsoft’s sample follows this structure with Azure AI Search for indexing and retrieval and Azure OpenAI for generating responses. It includes sample data and a working Python application, so you can follow the same workflow before replacing the sample documents with your own.

What you’ll need: Python, Azure OpenAI, Azure AI Search, a collection of documents, and an embedding model.

Source code/tutorial: RAG Chat App with Your Data – Microsoft Learn

Skills you’ll build: RAG architecture, document chunking, embeddings, retrieval, vector search, prompt orchestration, grounded responses.

15. Multi-Document Summarizer

Imagine you have several articles covering the same event. Each article may contain information that the others don’t, while some points may be repeated across all of them. Reading every article gives you the details, but you may still want one summary instead of going through each article separately.

Build a summarizer that takes these articles and produces one final summary. Start by dividing the documents into smaller sections and generating a summary for each section. Then pass those summaries to the LLM and ask it to identify the main points across them, remove repeated information, and write the final summary. If the documents are too large to process in one request, this approach lets you handle them in smaller batches.

LangChain provides a map-reduce summarization workflow for this process, with code that you can adapt to your own collection of documents.

What you’ll need: Python, an LLM, a collection of related documents, and LangChain.

Source code/tutorial: Summarize Documents with LangChain

Skills you’ll build: Multi-document summarization, document processing, context management, prompt design, LLM workflows.

16. Structured Information Extraction Pipeline

A resume might contain a person’s name, skills, education, and work history in completely different places. An invoice has its own set of fields, such as the invoice number, items, prices, and total amount. In both cases, the information is there, but it isn’t arranged in a format that another application can easily use.

Here, you can give an LLM a piece of unstructured text and define the fields you want it to return. For example, a receipt could be converted into fields such as order number, items, quantity, price, and total. Use a JSON Schema or Pydantic model to define that structure, then have the model extract the information and return it in that format. You can test the same approach on different types of documents and see how well the extracted fields match the source text.

Microsoft’s sample includes Python implementations for extracting structured information from text, PDFs, webpages, images, and GitHub content, so you can use one of these examples as the starting point for your pipeline.

What you’ll need: Python, an LLM with structured-output support, Pydantic, and sample documents or text to process.

Source code/tutorial: Entity Extraction with Azure OpenAI Structured Outputs – Microsoft Learn

Skills you’ll build: Structured information extraction, JSON outputs, schema design, Pydantic, document processing, prompt design.

17. Prompt Evaluation Harness

A prompt can give a good response for one question and a poor response for another. Changing a few words in the prompt can also change how the model responds. Testing it with a handful of examples isn’t enough if you want to know whether a new prompt is actually better.

Build a small test set with questions and expected answers, then run different versions of your prompt against the same examples. Record the responses and score them for things such as correctness, relevance, and consistency. You can then compare the results of each prompt version to avoid judging them one response at a time.

LangSmith provides this workflow through datasets, evaluators, and experiments. Its evaluation tools let you run the same application against a dataset, score the outputs, and compare different experiments.

What you’ll need: Python, an LLM API, LangSmith, a set of test inputs, and expected outputs or evaluation criteria.

Source code/tutorial: LangSmith Evaluation Quickstart

Skills you’ll build: Prompt evaluation, automated testing, benchmarking, LLM evaluation, experiment comparison.

18. LLM Guardrails & Moderation Layer

What happens when a user sends an unsafe request to your AI application? Or when the model generates a response that shouldn’t be shown to the user? A production application needs to check both sides of the interaction before allowing the request or response to continue.

Build a moderation layer that checks user prompts and model responses against a set of safety rules. You can start by detecting harmful content and setting thresholds for what should be allowed or blocked. Then add checks for issues such as prompt attacks and unsupported model responses. Microsoft Azure AI Content Safety provides APIs and Python examples for these checks, including severity-based filtering and Prompt Shields for detecting attacks in user inputs and documents.

Test the guardrails with safe and unsafe examples and see where the system blocks a request, allows it through, or flags it for further review.

What you’ll need: Python, Azure AI Content Safety, an Azure AI resource, and sample prompts and model responses to test.

Source code/tutorial: Implement generative AI guardrails with Azure AI Content Safety – Microsoft Learn

Skills you’ll build: LLM guardrails, content moderation, prompt attack detection, safety filtering, responsible AI, safety pipelines.

Datasets, Evaluation & the Metrics That Matter

Every NLP model is shaped by the data it's trained and tested on. A sentiment classifier needs labelled opinions, a summarization model requires reference summaries, while a RAG system depends on the quality of the documents it retrieves. Choosing the right dataset is the first step in building a reliable NLP application; evaluating it with the right metrics is what tells you whether it's actually performing as expected.

Where to Find NLP Datasets

You don't always need to collect or annotate your own data. Many NLP datasets are publicly available and cover tasks ranging from text classification to question answering and summarization.

• Hugging Face Datasets - Thousands of datasets for classification, summarization, translation, question answering, and more.
• Kaggle - Community-contributed datasets and NLP competitions.
• UCI Machine Learning Repository - Classic datasets for machine learning and NLP experiments.
• Common Crawl & Wikipedia Dumps - Large-scale text corpora for language modelling and information retrieval.
• Government open-data portals & research repositories - Useful for domain-specific projects in healthcare, finance, legal tech, and public policy.

Evaluation Metrics by NLP Task

NLP Task	Common Metrics	What They Do
Text Classification (Spam, Sentiment, Emotion Detection)	Accuracy, Precision, Recall, F1-score	Measures how well the model distinguishes between different classes while balancing false positives and false negatives.
Named Entity Recognition (NER)	Precision, Recall, F1-score	Evaluates how accurately entities are identified and extracted from text.
Semantic Search & Retrieval	Precision@K, Recall@K, MRR, nDCG	Measures whether the most relevant documents are retrieved and ranked highly.
Text Summarization	ROUGE, BLEU, Human Evaluation	Compares generated summaries against reference summaries while assessing readability and information coverage.
Question Answering	Exact Match (EM), F1-score	Evaluates whether the predicted answer matches the expected response and captures the correct context.
RAG Applications	Retrieval Accuracy, Faithfulness, Context Precision, Human Evaluation	Assesses both retrieval quality and whether responses remain grounded in the retrieved documents.

Beyond Benchmark Scores

Evaluation doesn't end with a benchmark score, especially for modern LLM applications. Several models may achieve similar metrics while producing noticeably different user experiences. That's why many production systems combine automated evaluation with human review to assess factors such as factual accuracy, relevance, completeness, and consistency.

When showcasing an nlp project, include not only the model and results but also the dataset you used, the evaluation metrics you tracked, and why those metrics were appropriate for the task. It demonstrates that you've thought beyond implementation and understand how NLP systems are assessed.

Choosing Projects for Final Year vs Job Portfolio

The project that works well for a college submission may not be the one you want at the top of your resume. An nlp projects for final year needs enough depth for you to spend time on the problem and defend your work. A portfolio needs projects that show different areas of NLP.

If You're Choosing a Final-Year Project

With a Resume Parser, you can work with resumes that use different formats and layouts, compare how well your parser extracts skills, education, and experience, and see where it starts making mistakes.

With a News Search Engine, you can test different search queries, compare how the results are ranked, and check whether the articles appearing at the top are actually relevant.

The same idea works for a Question Answering System or Multi-Document Summarizer. Pick a problem where there is something to test and improve after the first version works. That gives you material for your report and viva without forcing several unrelated techniques into one project.

If You're Building a Portfolio for Interviews

A portfolio is easier to judge when the projects don't all look the same.

You might start with a Spam Classifier using TF-IDF, then move to BERT Fine-Tuning for a transformer-based task. Semantic Search introduces embeddings and vector search, while RAG adds document retrieval and LLMs. Prompt Evaluation then covers a different part of the LLM workflow: checking whether the model is actually giving good responses.

You don't need every one of these projects. Pick the ones that fit the roles you're applying for and avoid filling the portfolio with five versions of text classification.

What Should You Know About a Project Before Adding It to Your Resume?

If your project uses TF-IDF, you should know how the text is converted into features and why you chose it. If you fine-tuned BERT, you should understand tokenization, attention, and what fine-tuning changes. If you built a RAG chatbot, you should know how the documents are split, how the relevant sections are retrieved, and what happens when the retrieved information is wrong.

The same applies to the other projects in this list. You don't have to know every library function by memory. You should understand the actual process you built well enough to explain it without going back to the tutorial.

This is the level of preparation that helps when the project moves from a GitHub repository to a viva or technical interview.

NLP vs LLM Engineering: Understanding the Difference

You have now worked through projects that range from spam classification and NER to RAG and LLM guardrails. But where do these projects fit when you start looking at NLP and LLM engineering jobs?

NLP Engineer and LLM Engineer are not completely separate roles. There is a lot of overlap, but the day-to-day work can be quite different.

An NLP Engineer may work on text classification, sentiment analysis, NER, document retrieval, or transformer models. An LLM Engineer may work on semantic search, RAG, structured extraction, prompt evaluation, or AI safety.

Career Path	Common Responsibilities	Projects from This Guide
NLP Engineer	Text classification, sentiment analysis, NER, document retrieval, transformer fine-tuning	Spam Email Classifier, Twitter Sentiment Analysis, Resume Parser, News Search Engine, BERT Sentiment Analysis
LLM Engineer	Semantic search, RAG, prompt engineering, structured extraction, evaluation, AI safety	Semantic Search, RAG Chatbot, Multi-Document Summarizer, Prompt Evaluation Harness, LLM Guardrails

You don't have to decide which title fits you right now. The projects themselves can help you figure that out. If you enjoy working with datasets, features, classification, extraction, and model performance, the earlier NLP projects give you more of that experience. If you prefer building applications around language models, the later projects move into retrieval, generation, evaluation, and safety.

There is also a reason not to skip the earlier projects just because LLMs are getting more attention. A RAG system still needs retrieval. Semantic search still needs embeddings. Any model still needs evaluation. The later projects use many of the ideas you encountered earlier, but in larger applications.

So you don't need to choose between “NLP” and “LLM” before you've had a chance to try both. Build across the two, see which work interests you, and then use that experience to decide what you want your portfolio to focus on.

If you want structured learning alongside these projects, you can explore Scaler's AI & ML Program.

FAQs

Q1. What are good NLP projects for beginners?

If you're new to NLP, start with projects that teach the fundamentals of text processing before moving on to transformer models. A spam email classifier using TF-IDF, Twitter sentiment analysis, and a Named Entity Recognition (NER) pipeline with spaCy are some good options to begin with because they introduce preprocessing, feature engineering, and text classification while remaining relevant for technical interviews.

Q2. Is classical NLP still worth learning in the LLM era?

Yes. Classical NLP techniques such as TF-IDF, text classification, and Named Entity Recognition are still widely used in production systems where speed, interpretability, and lower computational costs matter. They also provide the foundation for understanding transformer models and remain common topics in NLP and machine learning interviews.

Q3. Which NLP projects stand out in 2026 interviews?

Projects that solve real-world problems tend to make the strongest impression. A Retrieval-Augmented Generation (RAG) chatbot built on your own documents, a semantic search engine using embeddings, or an LLM evaluation framework demonstrate practical experience with modern NLP workflows while giving you meaningful architectural decisions to discuss during interviews.

Q4. Do NLP projects require a GPU?

Not always. Classical NLP projects such as text classification, sentiment analysis, and Named Entity Recognition can run comfortably on a standard laptop. Many transformer-based projects can be trained using free GPU resources on platforms like Google Colab, while several LLM applications rely on hosted APIs and therefore don't require a dedicated GPU at all.

Q5. How are NLP projects evaluated?

The evaluation metric depends on the task. Classification projects typically use Accuracy, Precision, Recall, and F1-score, while summarization models are commonly evaluated using ROUGE. Retrieval-based applications such as semantic search or RAG systems are measured using retrieval metrics like Precision@K or Recall@K, often supplemented with human evaluation to assess response quality and factual accuracy.

Q6. Which datasets are commonly used for NLP projects?

Popular NLP datasets include IMDb Reviews for sentiment analysis, TweetEval for social media classification, SQuAD for question answering, and CNN/DailyMail for text summarization. If you're building a RAG application, you can also use your own PDFs, documentation, research papers, or knowledge base as the source documents.