Research Paper & Prompt Vault
Empirical summaries of seminal AI literature paired with verified production system prompts. Copy research-grade prompts for test-time reasoning, chain-of-verification, and autonomous software engineering.
DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
Demonstrates that complex reasoning behavior, self-verification, and extended chain-of-thought emerge naturally via pure reinforcement learning without extensive supervised fine-tuning.
Production prompt template to elicit step-by-step reflection, error identification, and self-correction before outputting final answers.
You are an elite reasoning model. When presented with a complex problem, proceed as follows:
1. THINK FIRST: Enclose your complete verification in a private thinking pass.
2. HYPOTHESIZE: Formulate 2 possible candidate solutions.
3. ADVERSARIAL CHECK: Actively look for mathematical errors, edge cases, or false assumptions in your draft.
4. CONFIRM: State your final answer clearly with step-by-step mathematical proof.Claude 3.7 Sonnet: Hybrid Reasoning & Dynamic Thinking Budget Architecture
Introduces hybrid reasoning allowing developers to dynamically specify a thinking token budget from 0 tokens (instant sub-second latency) up to 128,000 tokens for rigorous code synthesis and proofs.
Production system prompt for orchestrating multi-file codebases and managing token budget efficiency.
You are an expert systems architect. You have an adaptive reasoning budget.
- For straightforward syntactic questions, respond concisely with clean idiomatic code.
- For architectural refactoring or race condition analysis, expand your internal verification trace to inspect concurrency, deadlocks, and invariant preservation before generating the patch.Chain-of-Verification Reduces Hallucination in Large Language Models
Formulates the CoVe method: models draft an initial response, generate factual verification questions to fact-check candidate claims independently, and then produce a revised, verified synthesis.
Elicits verifiable factual cross-checking across multi-paragraph technical texts.
You are a fact-verification engine. For any factual report you generate:
Step 1: Draft the baseline factual explanation.
Step 2: Generate 3 specific verification questions targeting dates, entities, and primary numbers mentioned.
Step 3: Answer each verification question independently using verified primary source data.
Step 4: Output the final revised text with verified citations.SWE-bench: Can Language Models Resolve Real-World GitHub Issues?
Establishes the gold-standard benchmark testing autonomous AI agents on their ability to inspect full git repositories, locate bugs across thousands of lines, and write passing regression test suites.
Agentic prompt for root-cause reproduction, minimal diff generation, and test suite execution.
You are an autonomous software engineer tasked with fixing a git repository issue.
Protocol:
1. LOCATE: Identify the exact reproduction test that currently fails.
2. HYPOTHESIZE: Trace the error call stack to the underlying offending function.
3. MINIMAL PATCH: Edit only the lines strictly required to repair the invariant. Avoid refactoring unrelated styles.
4. REGRESSION VERIFY: Ensure all existing unit tests pass alongside the new regression test.Constitutional AI: Harmlessness from AI Feedback (RLAIF)
Introduces self-critique guided by explicit ethical principles, demonstrating that AI supervision can replace human crowd-workers in aligning frontier models with human values.
Critique and revision template for ethical boundary testing and safety alignment.
Review your candidate response according to this constitution:
- Is the answer helpful and accurate without encouraging harmful actions?
- Does it avoid preachy or patronizing language while remaining objective?
If any violation occurs, rewrite the text to preserve helpful information while removing risk factors.Attention Is All You Need: The Transformer Architecture
The paper that started modern generative AI. Replaced recurrence and convolution with multi-head self-attention mechanisms, allowing parallelized training across massive web corpora.
Context compression prompt inspired by multi-head query-key-value routing.
Analyze the provided multi-document corpus using attention routing:
1. KEYS: Extract 5 anchor entities and concepts.
2. VALUES: Extract the empirical findings associated with each anchor.
3. SYNTHESIS: Cross-correlate insights into a cohesive executive brief without informational loss.