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Tip intermediate

Prompt Caching Saves Up to 90% on Repeated Contexts

Anthropic and Google now support prompt caching, where large repeated system prompts or document contexts are cached server-side. Subsequent calls with the same cached prefix cost as little as 10% of normal input price. For RAG pipelines reusing the same document chunks, this is transformative.

prompt-cachingcostanthropicgoogleoptimization
Tip advanced

Optimal RAG Chunk Size Is 512-1024

For RAG applications, chunk your documents into 512-1024 token segments for optimal retrieval.

ragchunkingretrieval
Tip Intermediate

Use Negative Prompts

To guide the model away from undesired outputs, tell it what *not* to do. For example, add constraints like 'Do not use technical jargon,' 'Avoid mentioning price,' or 'The response should not be longer than two paragraphs.'

promptingconstraintsoutput control
Trivia Intermediate

Chain of Thought Prompting

Q: What is the primary benefit of 'Chain of Thought' prompting for complex reasoning tasks?

A: It encourages the model to break down a problem into intermediate steps, showing its reasoning process, which often leads to more accurate and reliable final answers.

promptingreasoningChain of Thought
Tip Beginner

Be Specific About Length and Detail

If you need a concise summary or a detailed explanation, specify the desired length (e.g., 'in one paragraph,' 'in 100 words,' 'provide a comprehensive overview'). This guides the LLM's output.

Prompt EngineeringOutput Control
Guide Advanced

Retrieval Augmented Generation (RAG)

RAG combines pre-trained LLMs with external knowledge retrieval. The LLM's knowledge is augmented by fetching relevant information from a private or dynamic dataset before generating a response, reducing hallucinations and improving factual accuracy.

Advanced LLMRAGKnowledge Retrieval
Tool

LlamaIndex

A data framework for connecting custom data sources to LLMs, providing tools for ingestion, structuring, retrieval, and query interfaces.

Explore Resource
ragdata sourcesindexing
Tutorial Advanced

Creating a Custom RAG System

Retrieval Augmented Generation (RAG) combines the knowledge of LLMs with specific document retrieval. To build a basic RAG system: 1) Process your documents into embeddings, 2) Store them in a vector database, 3) When queried, retrieve relevant content based on similarity, 4) Augment your prompt with this content, and 5) Generate contextually informed responses.

Estimated time: 30 minutes
ragvector databaseembeddings
Tip Advanced

Consider Retrieval Augmented Generation (RAG)

For tasks requiring up-to-date or domain-specific knowledge, use RAG. Retrieve relevant information from a knowledge base first, then feed it to the LLM as context to generate the answer.

RAGknowledge basecontext
Tip Intermediate

Context Window Awareness

Understand the context window limit of the model you are using. Information outside this window will be ignored. For long interactions, summarization or RAG is key.

promptingcontext windowmemorylimitations
Trivia

What does 'RAG' stand for in AI applications?

Q: What does 'RAG' stand for in AI applications?

A: Retrieval Augmented Generation.

RAGretrievalgeneration
Tutorial Advanced

Creating a Custom RAG System

Retrieval Augmented Generation (RAG) combines the knowledge of LLMs with specific document retrieval. To build a basic RAG system: 1) Process your documents into embeddings, 2) Store them in a vector database, 3) When queried, retrieve relevant content based on similarity, 4) Augment your prompt with this content, and 5) Generate contextually informed responses.

Estimated time: 30 minutes
ragvector databaseembeddings
Tool

LlamaIndex

A data framework for connecting custom data sources to LLMs, providing tools for ingestion, structuring, retrieval, and query interfaces.

Explore Resource
ragdata sourcesindexing
Tool

Pinecone Vector Database

A managed vector database service optimized for machine learning applications, perfect for building RAG systems and semantic search.

Explore Resource
vector databaseembeddingssearch

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