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2026 Large Language Model Projects · Fine-Tuning · RAG · Alignment · Inference

Large Language Model Projects

Best final-year topics on LLMs — LoRA / QLoRA fine-tuning, RAG pipelines, instruction tuning, DPO alignment, efficient inference (vLLM, llama.cpp), multimodal models and rigorous evaluation for BE, BTech and MTech students.

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2026
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Fine-Tuning · LoRA RAG · Knowledge Alignment · DPO Inference · Serving Multimodal Evaluation

Large Language Model Projects — Fine-Tuning, RAG, Alignment & Evaluation

LLMs power chat, coding, research and domain assistants. Final-year projects that go beyond calling an API — fine-tuning with LoRA/QLoRA, building evaluated RAG systems, preference alignment, or efficient inference — demonstrate real systems and ML engineering skill.

Below are best topics across fine-tuning, RAG, alignment, inference optimisation, multimodal LLMs and evaluation, with tools used in industry and university labs (Hugging Face, PEFT, vLLM, LangChain, LlamaIndex).

Hugging Face PEFT / LoRA vLLM LlamaIndex LangChain TRL / DPO
# Large Language Model Project Topic Tools Used
✏️ Fine-Tuning — LoRA · QLoRA · Instruction Tuning
01Fine-TuneLoRA Fine-Tuning of an Open LLM on a Domain Instruction DatasetHF Transformers, PEFT, datasets
02Fine-TuneQLoRA 4-bit Fine-Tuning for Memory-Constrained GPUsPEFT, bitsandbytes, Transformers
03Fine-TuneInstruction-Tuning Pipeline with Custom System + User TemplatesTRL SFTTrainer, Axolotl / Unsloth
04Fine-TuneContinued Pre-Training on Domain Corpus then Instruction TuneTransformers, PEFT, custom data
05Fine-TuneMulti-Task Fine-Tuning (Summarisation + QA + Classification)PEFT, mixed datasets, eval suite
06Fine-TuneParameter-Efficient Comparison: LoRA vs Adapter vs Prompt TuningPEFT, same base model, metrics
07Fine-TuneCode LLM Fine-Tuning for a Specific Language or FrameworkPEFT, code datasets, HumanEval-style
📚 RAG — Retrieval-Augmented Generation
08RAGEnd-to-End RAG over PDF / Markdown Knowledge Base with CitationsLlamaIndex / LangChain, Chroma/FAISS
09RAGAdvanced RAG: Hybrid Search, Reranking and Query DecompositionLlamaIndex, cross-encoder, BM25
10RAGRAGAS-Style Evaluation of Faithfulness, Relevance and ContextRAGAS, LangChain, custom set
11RAGGraphRAG / Knowledge-Graph Augmented RetrievalLlamaIndex, Neo4j / NetworkX
12RAGMulti-Hop QA Agent with Iterative RetrievalLangGraph / LangChain, vector DB
13RAGCorrective / Self-Reflective RAG with Quality GatesLangGraph, graders, rewrite
14RAGMultimodal RAG (Text + Images / Tables from Documents)LlamaIndex, vision encoder, OCR
🎯 Alignment — Preference · DPO · Safety
15AlignDPO (Direct Preference Optimisation) on Preference PairsTRL DPOTrainer, PEFT
16AlignPreference Data Collection UI and Ranking PipelineStreamlit/Gradio, pairwise labels
17AlignSafety Fine-Tuning / Refusal Behaviour on Harmful PromptsPEFT, safety datasets, eval
18AlignConstitutional / Principle-Based Self-Critique LoopLangChain, critique prompts
19AlignComparison of SFT-only vs SFT+DPO on Helpfulness MetricsTRL, evaluation harness
⚡ Inference & Serving Optimisation
20InferHigh-Throughput Serving with vLLM (PagedAttention, Batching)vLLM, OpenAI-compatible API
21InferCPU / Edge Inference with llama.cpp and Quantized GGUF Modelsllama.cpp, GGUF, quant tools
22InferLatency vs Throughput Trade-off Study for a Fixed ModelvLLM / HF generate, benchmarks
23InferSpeculative Decoding or Draft-Model Speedup ExperimentvLLM / custom, draft model
24InferStructured Output / JSON Mode Reliability BenchmarkOutlines / guidance, parsers
25InferStreaming Chat UI with Token-Level Latency MetricsFastAPI/Streamlit, vLLM/HF
🖼️ Multimodal LLMs
26MultiVision-Language Model Fine-Tuning or Adapter for Document VQALLaVA / Qwen-VL style, PEFT
27MultiImage Captioning and Visual QA Pipeline with Open VLMsHF multimodal models, eval
28MultiMultimodal RAG: Retrieve Images + Text for Grounded AnswersLlamaIndex, CLIP embeddings
29MultiChart / Table Understanding with Vision-Language ModelsVLM APIs or open weights
📊 Evaluation · Benchmarks · Prompting
30EvalSystematic Prompt Engineering Study (Zero/Few-shot, CoT, ToT)Custom harness, LLM-as-judge
31EvalLLM-as-Judge Evaluation Pipeline with Agreement AnalysisGPT-4 / open judge, stats
32EvalDomain Benchmark Construction and Leaderboard for a TaskDatasets, metrics, scripts
33EvalHallucination Detection and Mitigation StrategiesRAGAS / custom detectors
34EvalCost–Quality Trade-off: Small vs Large Models on Fixed TasksMultiple models, token costs
35EvalRobustness to Prompt Perturbation and Adversarial InputsAttack suite, eval metrics
🏥 Domain-Specific LLM Applications
36DomainLegal / Contract Clause Extraction and Summarisation LLMLoRA or RAG, structured output
37DomainMedical Literature QA with Strict Citation RequirementsRAG, medical corpus, guardrails
38DomainEducational Tutor LLM with Curriculum-Aligned ResponsesFine-tune or RAG, quiz tools
39DomainCode Review and Documentation Generation AssistantCode LLM, repo tools
40DomainCustomer Support LLM with Tool Use and Escalation LogicLangGraph, RAG, tools
41DomainFinancial Report Analysis and Insight GenerationRAG over filings, structured out
🔬 Research-Oriented & Systems Topics
42ResearchCatastrophic Forgetting Measurement During Domain Fine-TuningPEFT, before/after benchmarks
43ResearchKV-Cache and Long-Context Behaviour AnalysisTransformers, long prompts
44ResearchReproducible LLM Experiment Protocol for Student ProjectsConfigs, seeds, logging (W&B)
45ResearchRetrieval Quality vs Generation Quality Ablation StudyRAGAS, controlled corpora

Topics reflect common university and industry practice with open models and Hugging Face tooling. Contact us for reference material, training/inference scripts, evaluation setup, university-format report, PPT and viva Q&A for any topic above.

Why Choose Us for LLM Projects?

Bangalore-based guidance for BE, BTech and MTech students working on fine-tuning, RAG and LLM systems.

Fine-Tuning

LoRA, QLoRA and instruction-tuning pipelines with PEFT, TRL and clear before/after evaluation.

RAG Systems

Hybrid retrieval, reranking, GraphRAG and RAGAS-style metrics for grounded, citable answers.

Alignment

DPO preference training, safety behaviours and principle-based critique loops with TRL.

Inference

vLLM serving, llama.cpp GGUF quantisation and latency–throughput studies for deployment.

Frequently Asked Questions — Large Language Model Projects

Top topics include LoRA/QLoRA domain fine-tuning, evaluated RAG systems, DPO alignment, vLLM/llama.cpp inference, multimodal document VQA, and systematic prompt and benchmark evaluation.
Hugging Face Transformers, PEFT, TRL; Axolotl/Unsloth for efficient training; vLLM and llama.cpp for inference; LangChain and LlamaIndex for RAG; Weights & Biases for tracking; optional OpenAI/Anthropic APIs as baselines.
Yes. Packages include reference material, training and inference scripts, dataset notes, evaluation metrics, demo UI where relevant, university-format report, PPT and viva Q&A.
Fine-tuning updates model weights (e.g. LoRA) so the model internalises new style or knowledge. RAG keeps the model fixed and retrieves documents at query time to ground answers. Many strong projects combine both.