| def build_answer_prompt( |
| question: str, |
| chapter: str, |
| section_title: str, |
| chunks: list[dict], |
| ) -> list[dict]: |
| """ |
| Builds the Groq chat messages for the ANSWER generation call. |
| |
| Returns a list of message dicts in OpenAI/Groq format: |
| [{"role": "system", ...}, {"role": "user", ...}] |
| """ |
| |
| context_blocks = "" |
| for i, chunk in enumerate(chunks, 1): |
| similarity_pct = int(chunk.get("similarity", 0) * 100) |
| context_blocks += ( |
| f"[{i}] (relevance: {similarity_pct}%) " |
| f"{chunk.get('chapter', '')} > {chunk.get('section_title', '')}\n" |
| f"{chunk.get('text_content', '').strip()}\n\n" |
| ) |
|
|
| system_prompt = ( |
| "You are an expert NEET Biology teacher with deep knowledge of NCERT textbooks.\n" |
| "Your job is to answer the student's question using ONLY the provided NCERT context below.\n" |
| "Rules:\n" |
| " 1. Base your answer strictly on the NCERT context provided.\n" |
| " 2. Do NOT introduce facts not present in the context.\n" |
| " 3. If the context does not contain enough information, say: " |
| "'The provided NCERT context does not fully address this question.'\n" |
| " 4. Keep the answer concise, factually accurate, and at NEET exam level.\n" |
| f" 5. The question belongs to the chapter: '{chapter}', topic: '{section_title}'." |
| ) |
|
|
| user_prompt = ( |
| f"Question: {question}\n\n" |
| f"NCERT Context:\n{context_blocks}" |
| "Answer:" |
| ) |
|
|
| return [ |
| {"role": "system", "content": system_prompt}, |
| {"role": "user", "content": user_prompt}, |
| ] |
|
|
|
|
| def build_justification_prompt( |
| question: str, |
| answer: str, |
| chunks: list[dict], |
| ) -> list[dict]: |
| """ |
| Builds the Groq chat messages for the JUSTIFICATION generation call. |
| The LLM must explain WHY the answer is correct, citing context points. |
| """ |
| context_blocks = "\n".join( |
| f"[{i}] {c.get('text_content', '').strip()}" |
| for i, c in enumerate(chunks, 1) |
| ) |
|
|
| system_prompt = ( |
| "You are an expert NEET Biology teacher.\n" |
| "Given the student's question, the answer, and NCERT context passages, " |
| "provide a step-by-step biological justification for why the answer is correct.\n" |
| "Rules:\n" |
| " 1. Use ONLY the NCERT context to justify.\n" |
| " 2. Number each reasoning step.\n" |
| " 3. Be specific — reference biological terms and mechanisms.\n" |
| " 4. Do NOT add facts not found in the context." |
| ) |
|
|
| user_prompt = ( |
| f"Question: {question}\n\n" |
| f"Answer Given: {answer}\n\n" |
| f"NCERT Context:\n{context_blocks}\n\n" |
| "Step-by-step justification:" |
| ) |
|
|
| return [ |
| {"role": "system", "content": system_prompt}, |
| {"role": "user", "content": user_prompt}, |
| ] |
|
|
|
|
| def build_verifier_prompt( |
| question: str, |
| answer: str, |
| chunks: list[dict], |
| ) -> list[dict]: |
| """ |
| Builds the Groq chat messages for the VERIFIER call. |
| The LLM must output YES or NO on whether the answer is supported by context. |
| """ |
| context_blocks = "\n".join( |
| f"[{i}] {c.get('text_content', '').strip()}" |
| for i, c in enumerate(chunks, 1) |
| ) |
|
|
| system_prompt = ( |
| "You are a strict NEET Biology answer verifier.\n" |
| "Given a question, an answer, and NCERT context passages, " |
| "determine if the answer is fully supported by the context.\n" |
| "Respond with EXACTLY one of:\n" |
| " YES - if the answer is clearly supported by the NCERT context.\n" |
| " NO - if the answer contains facts not present in the context, or contradicts it.\n" |
| "Then on a new line, give a one-sentence explanation." |
| ) |
|
|
| user_prompt = ( |
| f"Question: {question}\n\n" |
| f"Answer: {answer}\n\n" |
| f"NCERT Context:\n{context_blocks}\n\n" |
| "Verdict (YES/NO):" |
| ) |
|
|
| return [ |
| {"role": "system", "content": system_prompt}, |
| {"role": "user", "content": user_prompt}, |
| ] |
|
|
