Prompt Engineering

🧠 1. Prompt Generation & Dataset Creation

• Creating high-quality instruction-response pairs for LLM fine-tuning

• Generating few-shot and zero-shot prompts tailored to different domains

• Designing multi-turn conversation prompts for chatbots and assistants

• Building task-specific prompts (e.g., summarization, classification, translation)

✍️ 2. Prompt Annotation

• Human labeling of prompt quality, clarity, and relevance

• Annotating responses for helpfulness, factual accuracy, tone, and bias

• Adding metadata (e.g., task type, difficulty level, domain tag)

• Identifying and tagging hallucinations, inconsistencies, or safety issues

✅ 3. LLM Output Evaluation

• Human evaluation of LLM-generated responses using custom rubrics

• Scoring for fluency, correctness, creativity, and context awareness

• Comparative A/B testing between prompt versions or model outputs

• Red-teaming prompts to evaluate robustness and safety

🛠 4. RLHF Data Generation

• Generating datasets for Reinforcement Learning from Human Feedback (RLHF)

• Creating ranking annotations (e.g., Rank-2 or Rank-3) of LLM outputs

• Labeling responses with reward scores for model tuning

🧩 5. Prompt Template & Library Development

• Building domain-specific prompt templates (e.g., legal, medical, finance)

• Maintaining a prompt library with reusable, modular formats for client use

• Creating tool-specific prompt sets (for OpenAI, Anthropic, open-source models)

🔍 6. Prompt Safety & Ethics Review

• Annotation of prompts and completions for toxicity, bias, or unsafe content

• Filtering and flagging unsafe instructions or adversarial prompts

• Building safety datasets for LLM alignment and moderation

🌐 7. Multilingual Prompt Services

• Prompt creation and annotation in Arabic, English, and other supported languages

• Cross-lingual evaluation of prompts and translations for LLM applications