Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Uhandisi wa Maagizo× | Uzalishaji Ulioimarishwa kwa Urejeshaji (RAG)× | |
|---|---|---|
| Nyanja | Uchimbaji wa Matini | Uchimbaji wa Matini |
| Familia | Process / pipeline | Process / pipeline |
| Mwaka wa asili≠ | 2020 (few-shot prompting); 2022 (chain-of-thought) | 2020 |
| Mwanzilishi≠ | Tom Brown et al. (GPT-3 / few-shot framing, 2020); chain-of-thought extended by Jason Wei et al. (2022) | Lewis, Patrick et al. (Meta AI / Facebook AI Research) |
| Aina≠ | NLP pipeline — structured instruction design for large language models | Hybrid retrieval + generation pipeline |
| Chanzo asilia≠ | Brown, T. et al. (2020). Language Models are Few-Shot Learners. Advances in Neural Information Processing Systems (NeurIPS), 33, 1877-1901. link ↗ | Lewis, P. et al. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. Advances in Neural Information Processing Systems (NeurIPS), 33, 9459-9474. DOI ↗ |
| Majina mbadala≠ | instruction design, LLM prompting, Yönerge Mühendisliği (Prompt Engineering) | RAG, retrieval-augmented LLM, grounded generation, Erişim Destekli Metin Üretimi (RAG) |
| Zinazohusiana | 7 | 7 |
| Muhtasari≠ | Prompt engineering is the practice of crafting structured natural-language instructions — prompts — to elicit targeted outputs from large language models (LLMs). Formalised by Brown et al. (2020) in the context of GPT-3 and extended by Wei et al. (2022) with chain-of-thought prompting, it encompasses four main strategies: zero-shot, few-shot, chain-of-thought, and tree-of-thought. Rather than re-training a model, the analyst shapes the model's behaviour entirely through the design of the input text. | Retrieval-Augmented Generation (RAG) is a natural-language-processing pipeline introduced by Lewis et al. in 2020 that strengthens a large language model (LLM) with evidence fetched at inference time from an external knowledge base. Instead of relying solely on what a model memorised during training, RAG first retrieves the most relevant passages from a document index and then hands those passages to the LLM as context, grounding the generated answer in verifiable, up-to-date information. The approach reduces hallucination and allows domain-specific or time-sensitive knowledge to be injected without retraining the model. |
| ScholarGateSeti ya data ↗ |
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