diff --git a/src/data/roadmaps/prompt-engineering/content/chain-of-thought-cot-prompting@weRaJxEplhKDyFWSMeoyI.md b/src/data/roadmaps/prompt-engineering/content/chain-of-thought-cot-prompting@weRaJxEplhKDyFWSMeoyI.md index b1f8ad6ca..7341c650c 100644 --- a/src/data/roadmaps/prompt-engineering/content/chain-of-thought-cot-prompting@weRaJxEplhKDyFWSMeoyI.md +++ b/src/data/roadmaps/prompt-engineering/content/chain-of-thought-cot-prompting@weRaJxEplhKDyFWSMeoyI.md @@ -2,4 +2,6 @@ Chain of Thought prompting improves LLM reasoning by generating intermediate reasoning steps before providing the final answer. Instead of jumping to conclusions, the model "thinks through" problems step by step. Simply adding "Let's think step by step" to prompts often dramatically improves accuracy on complex reasoning tasks and mathematical problems. +Visit the following resources to learn more: + - [@video@Context Engineering vs. Prompt Engineering: Smarter AI with RAG & Agents](https://youtu.be/vD0E3EUb8-8?si=Y6MCLPzjmhMB4jSu&t=203) diff --git a/src/data/roadmaps/prompt-engineering/content/llm-self-evaluation@CvV3GIvQhsTvE-TQjTpIQ.md b/src/data/roadmaps/prompt-engineering/content/llm-self-evaluation@CvV3GIvQhsTvE-TQjTpIQ.md index f6692a248..675f67db8 100644 --- a/src/data/roadmaps/prompt-engineering/content/llm-self-evaluation@CvV3GIvQhsTvE-TQjTpIQ.md +++ b/src/data/roadmaps/prompt-engineering/content/llm-self-evaluation@CvV3GIvQhsTvE-TQjTpIQ.md @@ -2,5 +2,6 @@ LLM self-evaluation involves prompting models to assess their own outputs for quality, accuracy, or adherence to criteria. This technique can identify errors, rate confidence levels, or check if responses meet specific requirements. Self-evaluation helps improve output quality through iterative refinement and provides valuable feedback for prompt optimization. +Visit the following resources to learn more: - [@article@LLM Self-Evaluation](https://learnprompting.org/docs/reliability/lm_self_eval) diff --git a/src/data/roadmaps/prompt-engineering/content/llms-and-how-they-work@74JxgfJ_1qmVNZ_QRp9Ne.md b/src/data/roadmaps/prompt-engineering/content/llms-and-how-they-work@74JxgfJ_1qmVNZ_QRp9Ne.md index 4bdebf506..c1e23d98e 100644 --- a/src/data/roadmaps/prompt-engineering/content/llms-and-how-they-work@74JxgfJ_1qmVNZ_QRp9Ne.md +++ b/src/data/roadmaps/prompt-engineering/content/llms-and-how-they-work@74JxgfJ_1qmVNZ_QRp9Ne.md @@ -2,4 +2,6 @@ LLMs function as sophisticated prediction engines that process text sequentially, predicting the next token based on relationships between previous tokens and patterns from training data. They don't predict single tokens directly but generate probability distributions over possible next tokens, which are then sampled using parameters like temperature and top-K. The model repeatedly adds predicted tokens to the sequence, building responses iteratively. This token-by-token prediction process, combined with massive training datasets, enables LLMs to generate coherent, contextually relevant text across diverse applications and domains. +Visit the following resources to learn more: + - [@video@How Large Language Models Work](https://youtu.be/5sLYAQS9sWQ) diff --git a/src/data/roadmaps/prompt-engineering/content/one-shot--few-shot-prompting@Iufv_LsgUNls-Alx_Btlh.md b/src/data/roadmaps/prompt-engineering/content/one-shot--few-shot-prompting@Iufv_LsgUNls-Alx_Btlh.md index 12a305966..0496508f6 100644 --- a/src/data/roadmaps/prompt-engineering/content/one-shot--few-shot-prompting@Iufv_LsgUNls-Alx_Btlh.md +++ b/src/data/roadmaps/prompt-engineering/content/one-shot--few-shot-prompting@Iufv_LsgUNls-Alx_Btlh.md @@ -2,4 +2,6 @@ One-shot provides a single example to guide model behavior, while few-shot includes multiple examples (3-5) to demonstrate desired patterns. Examples show output structure, style, and tone, increasing accuracy and consistency. Use few-shot for complex formatting, specialized tasks, and when zero-shot results are inconsistent. +Visit the following resources to learn more: + - [@video@Context Engineering vs. Prompt Engineering: Smarter AI with RAG & Agents](https://youtu.be/vD0E3EUb8-8?si=Fi2igdPTBUocqnX7&t=177) diff --git a/src/data/roadmaps/prompt-engineering/content/prompt-debiasing@0H2keZYD8iTNyBgmNVhto.md b/src/data/roadmaps/prompt-engineering/content/prompt-debiasing@0H2keZYD8iTNyBgmNVhto.md index 95766b110..9428f02c3 100644 --- a/src/data/roadmaps/prompt-engineering/content/prompt-debiasing@0H2keZYD8iTNyBgmNVhto.md +++ b/src/data/roadmaps/prompt-engineering/content/prompt-debiasing@0H2keZYD8iTNyBgmNVhto.md @@ -1,6 +1,7 @@ # Prompt Debiasing -Prompt debiasing involves techniques to reduce unwanted biases in LLM outputs by carefully crafting prompts. This includes using neutral language, diverse examples, and explicit instructions to avoid stereotypes or unfair representations. Effective debiasing helps ensure AI outputs are more fair, inclusive, and representative across different groups and perspectives. +Prompt debiasing involves techniques to reduce unwanted biases in LLM outputs by carefully crafting prompts. This includes using neutral language, diverse examples, and explicit instructions to avoid stereotypes or unfair representations. Effective debiasing helps ensure AI outputs are fairer, inclusive, and more representative across different groups and perspectives. +Visit the following resources to learn more: - [@article@Prompt Debiasing](https://learnprompting.org/docs/reliability/debiasing) diff --git a/src/data/roadmaps/prompt-engineering/content/react-prompting@8Ks6txRSUfMK7VotSQ4sC.md b/src/data/roadmaps/prompt-engineering/content/react-prompting@8Ks6txRSUfMK7VotSQ4sC.md index 7da749f67..124728a79 100644 --- a/src/data/roadmaps/prompt-engineering/content/react-prompting@8Ks6txRSUfMK7VotSQ4sC.md +++ b/src/data/roadmaps/prompt-engineering/content/react-prompting@8Ks6txRSUfMK7VotSQ4sC.md @@ -2,4 +2,6 @@ ReAct (Reason and Act) prompting enables LLMs to solve complex tasks by combining reasoning with external tool interactions. It follows a thought-action-observation loop: analyze the problem, perform actions using external APIs, review results, and iterate until solved. Useful for research, multi-step problems, and tasks requiring current data. +Visit the following resources to learn more: + - [@video@4 Methods of Prompt Engineering](https://youtu.be/vD0E3EUb8-8?si=Y6MCLPzjmhMB4jSu&t=203) diff --git a/src/data/roadmaps/prompt-engineering/content/role-prompting@XHWKGaSRBYT4MsCHwV-iR.md b/src/data/roadmaps/prompt-engineering/content/role-prompting@XHWKGaSRBYT4MsCHwV-iR.md index 1a6457b9e..4dee79746 100644 --- a/src/data/roadmaps/prompt-engineering/content/role-prompting@XHWKGaSRBYT4MsCHwV-iR.md +++ b/src/data/roadmaps/prompt-engineering/content/role-prompting@XHWKGaSRBYT4MsCHwV-iR.md @@ -2,4 +2,6 @@ Role prompting assigns a specific character, identity, or professional role to the LLM to generate responses consistent with that role's expertise, personality, and communication style. By establishing roles like "teacher," "travel guide," or "software engineer," you provide the model with appropriate domain knowledge, perspective, and tone for more targeted, natural interactions. +Visit the following resources to learn more: + - [@video@Context Engineering vs. Prompt Engineering: Smarter AI with RAG & Agents](https://youtu.be/vD0E3EUb8-8?si=9orzEniOGmRD7g-o&t=136) diff --git a/src/data/roadmaps/prompt-engineering/content/structured-outputs@j-PWO-ZmF9Oi9A5bwMRto.md b/src/data/roadmaps/prompt-engineering/content/structured-outputs@j-PWO-ZmF9Oi9A5bwMRto.md index 133637094..c23a3bf5d 100644 --- a/src/data/roadmaps/prompt-engineering/content/structured-outputs@j-PWO-ZmF9Oi9A5bwMRto.md +++ b/src/data/roadmaps/prompt-engineering/content/structured-outputs@j-PWO-ZmF9Oi9A5bwMRto.md @@ -1,3 +1,7 @@ # Structured Outputs -Structured outputs involve prompting LLMs to return responses in specific formats like JSON, XML, or other organized structures rather than free-form text. This approach forces models to organize information systematically, reduces hallucinations by imposing format constraints, enables easy programmatic processing, and facilitates integration with applications. For example, requesting movie classification results as JSON with specified schema ensures consistent, parseable responses. Structured outputs are particularly valuable for data extraction, API integration, and applications requiring reliable data formatting. \ No newline at end of file +Structured outputs involve prompting LLMs to return responses in specific formats like JSON, XML, or other organized structures rather than free-form text. This approach forces models to organize information systematically, reduces hallucinations by imposing format constraints, enables easy programmatic processing, and facilitates integration with applications. For example, requesting movie classification results as JSON with a specified schema ensures consistent, parseable responses. Structured outputs are particularly valuable for data extraction, API integration, and applications requiring reliable data formatting. + +Visit the following resources to learn more: + +- [@article@Generating Structured Outputs from LLMs](https://towardsdatascience.com/generating-structured-outputs-from-llms/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration) \ No newline at end of file