3 Ways to Improve LLM Answer Quality
The short answer
To get noticeably better responses from any large-language model you can pull three main levers:
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Prompt engineering – craft clearer, more structured prompts.
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Retrieval-Augmented Generation (RAG) – give the model fresh, task-specific documents at run time.
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Parameter-efficient fine-tuning (LoRA / DoRA) – train small “adapter” weights so the model internalises your domain.
comparasion
| Approach | What it is | Key benefit | Main challenge | Java-friendly tooling |
|---|---|---|---|---|
| Prompt engineering | Writing instructions, examples and constraints inside the prompt so the frozen model behaves as you want | Instant improvement—no retraining or new infra needed | Fragile and iterative: vague wording, token limits and hallucinations still bite. | Spring AI & langeChain |
| RAG | A pipeline that first retrieves relevant document chunks (via embeddings search) and then injects them into the prompt before generation | Up-to-date, source-grounded answers; fewer hallucinations | Needs a vector store, retrieval logic and careful filtering; bad snippets = bad answers | Weaviate or Lucene for retrieval + DJL / Spring AI for generation; projects like LangChain4j wrap the pattern |
| LoRA / DoRA fine-tuning | Train tiny low-rank adapter matrices while keeping the original model frozen (LoRA); DoRA further freezes the sign of each weight and only learns magnitudes, improving accuracy at the same cost | Lets you specialise a huge model on modest hardware—only a few million extra parameters | Requires task data and some GPU time; still early-days for full Java workflows | Fine-tune with PEFT in Python, then load the merged weights with DJL; or call the tuned model over gRPC/HTTP. |
Take-aways
- Start with prompts—it’s free and immediate.
- Add RAG when you need current or proprietary knowledge.
- Fine-tune with LoRA/DoRA when the model must internalise your tone, policy or task logic.
Mastering all three techniques gives you a flexible toolbox for squeezing the best possible answers out of any LLM.
References
Last Updated: 2014 May