mirror of
https://github.com/kamranahmedse/developer-roadmap.git
synced 2026-03-12 17:51:53 +08:00
chore: sync content to repo (#9677)
Co-authored-by: kamranahmedse <4921183+kamranahmedse@users.noreply.github.com>
This commit is contained in:
github-actions[bot]
committed by
GitHub
GitHub
parent
a27d607e79
commit
b0878c3481
@@ -6,4 +6,5 @@ Visit the following resources to learn more:
|
||||
|
||||
- [@article@Top 15 Use Cases Of AI Agents In Business](https://www.ampcome.com/post/15-use-cases-of-ai-agents-in-business)
|
||||
- [@article@A Brief Guide on AI Agents: Benefits and Use Cases](https://www.codica.com/blog/brief-guide-on-ai-agents/)
|
||||
- [@video@The Complete Guide to Building AI Agents for Beginners](https://youtu.be/MOyl58VF2ak?si=-QjRD_5y3iViprJX)
|
||||
- [@video@The Complete Guide to Building AI Agents for Beginners](https://youtu.be/MOyl58VF2ak?si=-QjRD_5y3iViprJX)
|
||||
- [@article@How to Build Effective AI Agents to Process Millions of Requests](https://towardsdatascience.com/how-to-build-effective-ai-agents-to-process-millions-of-requests/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)
|
||||
@@ -6,4 +6,5 @@ Visit the following resources to learn more:
|
||||
|
||||
- [@article@Building an AI Agent Tutorial - LangChain](https://python.langchain.com/docs/tutorials/agents/)
|
||||
- [@article@AI Agents and Their Types](https://play.ht/blog/ai-agents-use-cases/)
|
||||
- [@article@How to Design My First AI Agent](https://towardsdatascience.com/how-to-design-my-first-ai-agent/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)
|
||||
- [@video@The Complete Guide to Building AI Agents for Beginners](https://youtu.be/MOyl58VF2ak?si=-QjRD_5y3iViprJX)
|
||||
@@ -6,4 +6,5 @@ Visit the following resources to learn more:
|
||||
|
||||
- [@article@What does an AI Engineer do?](https://www.codecademy.com/resources/blog/what-does-an-ai-engineer-do/)
|
||||
- [@article@What is an ML Engineer?](https://www.coursera.org/articles/what-is-machine-learning-engineer)
|
||||
- [@article@Machine Learning vs AI Engineer: What Are the Differences?](https://towardsdatascience.com/machine-learning-vs-ai-engineer-no-confusing-jargon/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)
|
||||
- [@video@AI vs ML](https://www.youtube.com/watch?v=4RixMPF4xis)
|
||||
@@ -5,4 +5,5 @@ AI (Artificial Intelligence) refers to systems designed to perform specific task
|
||||
Visit the following resources to learn more:
|
||||
|
||||
- [@article@What is AGI?](https://aws.amazon.com/what-is/artificial-general-intelligence/)
|
||||
- [@article@The crucial difference between AI and AGI](https://www.forbes.com/sites/bernardmarr/2024/05/20/the-crucial-difference-between-ai-and-agi/)
|
||||
- [@article@The crucial difference between AI and AGI](https://www.forbes.com/sites/bernardmarr/2024/05/20/the-crucial-difference-between-ai-and-agi/)
|
||||
- [@article@Stop Worrying about AGI: The Immediate Danger is Reduced General Intelligence (RGI)](https://towardsdatascience.com/stop-worrying-about-agi-the-immediate-danger-is-reduced-general-intelligence-rgi/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)
|
||||
@@ -4,4 +4,5 @@ Anomaly detection with embeddings works by transforming data, such as text, imag
|
||||
|
||||
Visit the following resources to learn more:
|
||||
|
||||
- [@article@Anomaly in Embeddings](https://ai.google.dev/gemini-api/tutorials/anomaly_detection)
|
||||
- [@article@Anomaly in Embeddings](https://ai.google.dev/gemini-api/tutorials/anomaly_detection)
|
||||
- [@article@Boosting Your Anomaly Detection With LLMs](https://towardsdatascience.com/boosting-your-anomaly-detection-with-llms/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)
|
||||
@@ -7,4 +7,5 @@ Visit the following resources to learn more:
|
||||
- [@official@Claude](https://claude.ai)
|
||||
- [@course@Claude 101](https://anthropic.skilljar.com/claude-101)
|
||||
- [@video@How To Use Claude Pro For Beginners](https://www.youtube.com/watch?v=J3X_JWQkvo8)
|
||||
- [@article@How To Use Claude Pro For Beginners](https://www.youtube.com/watch?v=J3X_JWQkvo8)
|
||||
- [@video@Claude FULL COURSE 1 HOUR (Build & Automate Anything)](https://www.youtube.com/watch?v=KrKhfm2Xuho)
|
||||
@@ -4,6 +4,6 @@ Open-source models are freely available for customization and collaboration, pro
|
||||
|
||||
Visit the following resources to learn more:
|
||||
|
||||
- [@article@Open-Source LLMs vs Closed: Unbiased Guide for Innovative Companies [2026]](https://hatchworks.com/blog/gen-ai/open-source-vs-closed-llms-guide/)
|
||||
- [@article@Open-Source LLMs vs Closed: Unbiased Guide for Innovative Companies [2026](https://hatchworks.com/blog/gen-ai/open-source-vs-closed-llms-guide/)
|
||||
- [@video@Open Source vs Closed AI: LLMs, Agents & the AI Stack Explained](https://www.youtube.com/watch?v=_QfxGZGITGw)
|
||||
- [@video@Open-Source vs Closed-Source LLMs](https://www.youtube.com/watch?v=710PDpuLwOc)
|
||||
@@ -5,4 +5,4 @@ Context compaction is a technique used to reduce the length of the context provi
|
||||
Visit the following resources to learn more:
|
||||
|
||||
- [@article@Context Engineering](https://blog.langchain.com/context-engineering-for-agents/)
|
||||
- [@opensource@Context Compaction](https://gist.github.com/badlogic/cd2ef65b0697c4dbe2d13fbecb0a0a5f)
|
||||
- [@article@Context Compaction](https://gist.github.com/badlogic/cd2ef65b0697c4dbe2d13fbecb0a0a5f)
|
||||
@@ -6,4 +6,5 @@ Visit the following resources to learn more:
|
||||
|
||||
- [@article@Context Engineering Guide](https://www.promptingguide.ai/guides/context-engineering-guide)
|
||||
- [@article@Effective context engineering for AI agents](https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents)
|
||||
- [@article@How to Perform Effective Agentic Context Engineering](https://towardsdatascience.com/how-to-perform-effective-agentic-context-engineering/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)
|
||||
- [@video@Context Engineering vs. Prompt Engineering: Smarter AI with RAG & Agents](https://www.youtube.com/watch?v=vD0E3EUb8-8)
|
||||
@@ -1,8 +1,7 @@
|
||||
# External Memory for LLMs
|
||||
# External Memory
|
||||
|
||||
External memory refers to the techniques used to provide Large Language Models (LLMs) with access to information that is not stored directly within their parameters. This allows LLMs to access and utilize a much broader and more up-to-date knowledge base than what was available during their training. By using external memory, LLMs can overcome limitations related to knowledge cut-off, hallucination, and the inability to incorporate new information, leading to more accurate, reliable, and contextually relevant respons
|
||||
External memory, in the context of large language models (LLMs), refers to mechanisms that allow these models to access and utilize information stored outside of their internal parameters. This can involve retrieving relevant data from databases, knowledge graphs, or other external sources during the prompt processing or generation phases to augment the model's knowledge and improve its performance on specific tasks. This enhances the LLM's ability to handle complex queries and generate more accurate and contextually relevant responses.
|
||||
|
||||
Visit the following resources to learn more:
|
||||
|
||||
- [@article@Context Engineering - LLM Memory and Retrieval for AI Agents](https://weaviate.io/blog/context-engineering)
|
||||
- [@article@4 context engineering strategies every AI engineer needs to know](https://newsletter.owainlewis.com/i/180013006/1-write-external-memory)
|
||||
- [@article@How to Maximize Agentic Memory for Continual Learning](https://towardsdatascience.com/how-to-maximize-agentic-memory-for-continual-learning/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)
|
||||
@@ -1,9 +1,10 @@
|
||||
# Fine-tuning
|
||||
|
||||
Fine-tuning involves taking a pre-trained large language model (LLM) and further training it on a smaller, task-specific dataset. This adapts the LLM to perform better on a particular task or domain. However, fine-tuning can be resource-intensive and may not always be the most efficient approach. Prompt engineering, retrieval-augmented generation (RAG), or using smaller, specialized models can sometimes achieve comparable or even better results with less computational overhead and data requirements.
|
||||
Fine-tuning involves taking a pre-trained large language model (LLM) and further training it on a smaller, task-specific dataset. This adapts the LLM to perform better on a particular task or domain. However, fine-tuning can be resource-intensive and may not always be the most efficient approach. Prompt engineering, retrieval-augmented generation (RAG), or using smaller, specialized models can sometimes achieve comparable or even better results with less computational overhead and data requirements.
|
||||
|
||||
Visit the following resources to learn more:
|
||||
|
||||
- [@article@What is fine-tuning?](https://www.ibm.com/think/topics/fine-tuning)
|
||||
- [@article@What is fine-tuning? A guide to fine-tuning LLMs](https://cohere.com/blog/fine-tuning)
|
||||
- [@article@How I Fine-Tuned Granite-Vision 2B to Beat a 90B Model — Insights and Lessons Learned](https://towardsdatascience.com/how-i-fine-tuned-granite-vision-2b-to-beat-a-90b-model-insights-and-lessons-learned/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)
|
||||
- [@video@RAG vs Fine-Tuning vs Prompt Engineering: Optimizing AI Models](https://www.youtube.com/watch?v=zYGDpG-pTho)
|
||||
@@ -4,6 +4,6 @@ The Google Agent Development Kit (ADK) is a framework designed to help developer
|
||||
|
||||
Visit the following resources to learn more:
|
||||
|
||||
- [@course@ADK Crash Course - From Beginner To Expert](https://codelabs.developers.google.com/onramp/instructions#0)
|
||||
- [@official@Agent Development Kit](https://google.github.io/adk-docs/)
|
||||
- [@official@Overview of Agent Development Kit](https://docs.cloud.google.com/agent-builder/agent-development-kit/overview)
|
||||
- [@course@ADK Crash Course - From Beginner To Expert](https://codelabs.developers.google.com/onramp/instructions#0)
|
||||
- [@official@Overview of Agent Development Kit](https://docs.cloud.google.com/agent-builder/agent-development-kit/overview)
|
||||
@@ -1,6 +1,6 @@
|
||||
# Google Gemini
|
||||
# Google's Gemini
|
||||
|
||||
Google Gemini is a family of multimodal large language models (LLMs) developed by Google AI. It's designed to understand and generate content across various modalities, including text, images, audio, and video. Gemini comes in different sizes and capabilities, allowing developers to choose the best model for their specific needs and resource constraints.
|
||||
Google Gemini is an advanced AI model by Google DeepMind, designed to integrate natural language processing with multimodal capabilities, enabling it to understand and generate not just text but also images, videos, and other data types. It combines generative AI with reasoning skills, making it effective for complex tasks requiring logical analysis and contextual understanding.
|
||||
|
||||
Visit the following resources to learn more:
|
||||
|
||||
|
||||
@@ -1,9 +1,10 @@
|
||||
# Haystack
|
||||
# Langchain
|
||||
|
||||
Haystack is an open-source Python framework that helps you build search and question-answering agents fast. You connect your data sources, pick a language model, and set up pipelines that find the best answer to a user’s query. Haystack handles tasks such as indexing documents, retrieving passages, running the model, and ranking results. It works with many back-ends like Elasticsearch, OpenSearch, FAISS, and Pinecone, so you can scale from a laptop to a cluster. You can add features like summarization, translation, and document chat by dropping extra nodes into the pipeline. The framework also offers REST APIs, a web UI, and clear tutorials, making it easy to test and deploy your agent in production.
|
||||
|
||||
Visit the following resources to learn more:
|
||||
|
||||
- [@official@Haystack](https://haystack.deepset.ai/)
|
||||
- [@official@@Haystack Overview](https://docs.haystack.deepset.ai/docs/intro)
|
||||
- [@official@Haystack Overview](https://docs.haystack.deepset.ai/docs/intro)
|
||||
- [@opensource@deepset-ai/haystack](https://github.com/deepset-ai/haystack)
|
||||
@@ -4,5 +4,5 @@ The Hugging Face Hub is a central platform where users can discover, share, and
|
||||
|
||||
Visit the following resources to learn more:
|
||||
|
||||
- [@official@Hugging Face Documentation](https://huggingface.co/docs/hub/en/index)
|
||||
- [@course@The Hugging Face Hub (LLM Course)](https://huggingface.co/learn/nlp-course/en/chapter4/1)
|
||||
- [@course@The Hugging Face Hub (LLM Course)](https://huggingface.co/learn/nlp-course/en/chapter4/1)
|
||||
- [@official@Hugging Face Documentation](https://huggingface.co/docs/hub/en/index)
|
||||
@@ -4,6 +4,6 @@ Hugging Face is a leading AI company and open-source platform that provides tool
|
||||
|
||||
Visit the following resources to learn more:
|
||||
|
||||
- [@official@Hugging Face](https://huggingface.co)
|
||||
- [@course@Hugging Face Official Video Course](https://www.youtube.com/watch?v=00GKzGyWFEs&list=PLo2EIpI_JMQvWfQndUesu0nPBAtZ9gP1o)
|
||||
- [@official@Hugging Face](https://huggingface.co)
|
||||
- [@video@What is Hugging Face? - Machine Learning Hub Explained](https://www.youtube.com/watch?v=1AUjKfpRZVo)
|
||||
@@ -6,5 +6,6 @@ Visit the following resources to learn more:
|
||||
|
||||
- [@article@What is a large language model (LLM)?](https://www.cloudflare.com/en-gb/learning/ai/what-is-large-language-model/)
|
||||
- [@article@Understanding AI: Everything you need to know about language models](https://leerob.com/ai)
|
||||
- [@article@New to LLMs? Start Here](https://towardsdatascience.com/new-to-llms-start-here/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)
|
||||
- [@video@How Large Language Models Work](https://www.youtube.com/watch?v=5sLYAQS9sWQ)
|
||||
- [@video@Large Language Models (LLMs) - Everything You NEED To Know](https://www.youtube.com/watch?v=osKyvYJ3PRM)
|
||||
@@ -4,5 +4,5 @@ Meta Llama is a family of large language models (LLMs) developed by Meta AI. The
|
||||
|
||||
Visit the following resources to learn more:
|
||||
|
||||
- [@official@Llama](https://www.llama.com/)
|
||||
- [@course@Building with Llama 4](https://www.deeplearning.ai/short-courses/building-with-llama-4/)
|
||||
- [@course@Building with Llama 4](https://www.deeplearning.ai/short-courses/building-with-llama-4/)
|
||||
- [@official@Llama](https://www.llama.com/)
|
||||
@@ -6,4 +6,5 @@ Visit the following resources to learn more:
|
||||
|
||||
- [@roadmap@Visit Dedicated Prompt Engineering Roadmap](https://roadmap.sh/prompt-engineering)
|
||||
- [@article@hat is Prompt Engineering? - AI Prompt Engineering Explained - AWS](https://aws.amazon.com/what-is/prompt-engineering/)
|
||||
- [@article@Advanced Prompt Engineering for Data Science Projects](https://towardsdatascience.com/advanced-prompt-engineering-for-data-science-projects/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)
|
||||
- [@video@What is Prompt Engineering?](https://www.youtube.com/watch?v=nf1e-55KKbg)
|
||||
@@ -7,4 +7,5 @@ Visit the following resources to learn more:
|
||||
- [@article@Context engineering vs. prompt engineering](https://www.elastic.co/search-labs/blog/context-engineering-vs-prompt-engineering)
|
||||
- [@article@Effective context engineering for AI agents](https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents)
|
||||
- [@article@Context Engineering vs Prompt Engineering](https://medium.com/data-science-in-your-pocket/context-engineering-vs-prompt-engineering-379e9622e19d)
|
||||
- [@article@Beyond Prompting: The Power of Context Engineering](https://towardsdatascience.com/beyond-prompting-the-power-of-context-engineering/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)
|
||||
- [@video@Context Engineering vs. Prompt Engineering: Smarter AI with RAG & Agents](https://www.youtube.com/watch?v=vD0E3EUb8-8)
|
||||
@@ -5,4 +5,5 @@ A vector database is designed to store, manage, and retrieve high-dimensional ve
|
||||
Visit the following resources to learn more:
|
||||
|
||||
- [@article@What is a Vector Database? Top 12 Use Cases](https://lakefs.io/blog/what-is-vector-databases/)
|
||||
- [@article@Vector Databases: Intro, Use Cases](https://www.v7labs.com/blog/vector-databases)
|
||||
- [@article@Vector Databases: Intro, Use Cases](https://www.v7labs.com/blog/vector-databases)
|
||||
- [@article@When (Not) to Use Vector DB](https://towardsdatascience.com/when-not-to-use-vector-db/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)
|
||||
@@ -5,4 +5,5 @@ Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by pr
|
||||
Visit the following resources to learn more:
|
||||
|
||||
- [@article@4 context engineering strategies every AI engineer needs to know](https://newsletter.owainlewis.com/p/4-context-engineering-strategies)
|
||||
- [@article@Context Engineering](https://blog.langchain.com/context-engineering-for-agents/)
|
||||
- [@article@Context Engineering](https://blog.langchain.com/context-engineering-for-agents/)
|
||||
- [@article@Is RAG Dead? The Rise of Context Engineering and Semantic Layers for Agentic AI](https://towardsdatascience.com/beyond-rag/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)
|
||||
@@ -6,4 +6,5 @@ Visit the following resources to learn more:
|
||||
|
||||
- [@article@Retrieval augmented generation use cases: Transforming data into insights](https://www.glean.com/blog/retrieval-augmented-generation-use-cases)
|
||||
- [@article@Retrieval Augmented Generation (RAG) – 5 Use Cases](https://theblue.ai/blog/rag-news/)
|
||||
- [@video@Introduction to RAG](https://www.youtube.com/watch?v=LmiFeXH-kq8&list=PL-pTHQz4RcBbz78Z5QXsZhe9rHuCs1Jw-)
|
||||
- [@video@Introduction to RAG](https://www.youtube.com/watch?v=LmiFeXH-kq8&list=PL-pTHQz4RcBbz78Z5QXsZhe9rHuCs1Jw-)
|
||||
- [@article@How to Train a Chatbot Using RAG and Custom Data](https://towardsdatascience.com/how-to-train-a-chatbot-using-rag-and-custom-data/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)
|
||||
@@ -5,4 +5,5 @@ Retrieval-Augmented Generation (RAG) is an AI approach that combines information
|
||||
Visit the following resources to learn more:
|
||||
|
||||
- [@article@What is Retrieval-Augmented Generation? - Google](https://cloud.google.com/use-cases/retrieval-augmented-generation)
|
||||
- [@article@RAG Explained: Understanding Embeddings, Similarity, and Retrieval](https://towardsdatascience.com/rag-explained-understanding-embeddings-similarity-and-retrieval/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)
|
||||
- [@video@What is Retrieval-Augmented Generation? - IBM](https://www.youtube.com/watch?v=T-D1OfcDW1M)
|
||||
@@ -1,9 +1,8 @@
|
||||
# Tokens
|
||||
# Tokens in Large Language Models
|
||||
|
||||
Tokens are the fundamental building blocks of large language models (LLMs). They are discrete units of text that the model processes and uses to understand and generate language. These units can be words, parts of words, or even individual characters, depending on the model's vocabulary. LLMs work by predicting the next token in a sequence, based on the preceding tokens and their learned patterns.
|
||||
Tokens are fundamental units of text that LLMs process, created by breaking text into smaller components such as words, subwords, or characters. Understanding tokens is crucial because models predict the next token in sequences, API costs are based on token count, and models have maximum token limits for input and output.
|
||||
|
||||
Visit the following resources to learn more:
|
||||
|
||||
- [@article@Explaining Tokens — the Language and Currency of AI](https://blogs.nvidia.com/blog/ai-tokens-explained/)
|
||||
- [@article@Understanding Tokens and Parameters in Model Training: A Deep Dive](https://www.functionize.com/blog/understanding-tokens-and-parameters-in-model-training)
|
||||
- [@video@Most devs don't understand how LLM tokens work](https://www.youtube.com/watch?v=nKSk_TiR8YA&t=33s)
|
||||
- [@article@Understanding Tokens and Parameters in Model Training: A Deep Dive](ttps://www.functionize.com/blog/understanding-tokens-and-parameters-in-model-training)
|
||||
@@ -4,7 +4,7 @@ Tools and function calling equip AI agents with the ability to interact with the
|
||||
|
||||
Visit the following resources to learn more:
|
||||
|
||||
- [@article@A Comprehensive Guide to Function Calling in LLMs](https://thenewstack.io/a-comprehensive-guide-to-function-calling-in-llms/)
|
||||
- [@article@What are Tools? - Hugging Face](https://huggingface.co/learn/agents-course/en/unit1/tools)
|
||||
- [@course@A Comprehensive Guide to Function Calling in LLMs](https://thenewstack.io/a-comprehensive-guide-to-function-calling-in-llms/)
|
||||
- [@official@What are Tools? - Hugging Face](https://huggingface.co/learn/agents-course/en/unit1/tools)
|
||||
- [@article@Compare 50+ AI Agent Tools in 2026](https://aimultiple.com/ai-agent-tools)
|
||||
- [@article@AI Agents Explained in Simple Terms for Beginners](https://www.geeky-gadgets.com/ai-agents-explained-for-beginners/)
|
||||
@@ -5,4 +5,5 @@ AI engineers are professionals who specialize in designing, developing, and impl
|
||||
Visit the following resources to learn more:
|
||||
|
||||
- [@article@How to Become an AI Engineer: Duties, Skills, and Salary](https://www.simplilearn.com/tutorials/artificial-intelligence-tutorial/how-to-become-an-ai-engineer)
|
||||
- [@article@AI Engineers: What they do and how to become one](https://www.techtarget.com/whatis/feature/How-to-become-an-artificial-intelligence-engineer)
|
||||
- [@article@AI Engineers: What they do and how to become one](https://www.techtarget.com/whatis/feature/How-to-become-an-artificial-intelligence-engineer)
|
||||
- [@article@I Transitioned from Data Science to AI Engineering: Here’s Everything You Need to Know](https://towardsdatascience.com/i-transitioned-from-data-science-to-ai-engineering-heres-everything-you-need-to-know/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)
|
||||
@@ -1,10 +1,9 @@
|
||||
# Zero-Shot Prompting
|
||||
# Zero Shot Prompting
|
||||
|
||||
Zero-shot prompting is a prompt engineering method that relies on the pretraining of a large language model (LLM) to infer an appropriate response. In contrast to other prompt engineering methods, such as few-shot prompting, models aren’t provided with examples of output when prompting with the zero-shot technique.1
|
||||
Zero-shot prompting is a prompt engineering method that relies on the pretraining of a large language model (LLM) to infer an appropriate response. In contrast to other prompt engineering methods, such as few-shot prompting, models aren’t provided with examples of output when prompting with the zero-shot technique.
|
||||
|
||||
Visit the following resources to learn more:
|
||||
|
||||
- [@article@What is zero-shot prompting?](https://www.ibm.com/think/topics/zero-shot-prompting)
|
||||
- [@article@Zero-Shot Prompting](https://www.promptingguide.ai/techniques/zeroshot)
|
||||
- [@article@Technique #3: Examples in Prompts: From Zero-Shot to Few-Shot](https://learnprompting.org/docs/basics/few_shot)
|
||||
- [@video@Zero-shot, One-shot and Few-shot Prompting Explained | Prompt Engineering 101](https://www.youtube.com/watch?v=sW5xoicq5TY)
|
||||
Reference in New Issue
Block a user