Combine static LLMs with dynamic search, Fetch relevant documents and inject them into prompts, Improve factual accuracy and reduce hallucinations

Ingest domain-specific documents, Generate vector embeddings, Perform semantic search and pass results to LLM

Convert text to high-dimensional vectors, Explore cosine similarity and vector distance, Use embeddings for search, clustering, and classification

Set up chains connecting LLMs to retrievers, Use agents, tools, and memory modules, Integrate with OpenAI, Chroma, FAISS

Use recursive and character-based splitters, Find optimal chunk size for recall vs. context window, Tune overlap and granularity

Generate embeddings using OpenAI, Store in ChromaDB or FAISS, Query using semantic similarity

Normalize vectors and cache embeddings, Store vectors with metadata for filtering

Use dimensionality reduction to visualize clusters, Debug poor retrieval with visual feedback

Build a chatbot that retrieves context on demand, Add memory and tools for dynamic interactions

Maintain context over turns, Dynamically fetch supporting content

Build a Q&A bot for docs/support pages, Run queries in real time and serve responses

Use batching, caching, and parallel processing, Design for low-latency, high-throughput use cases

Improve low-quality retrieval, Resolve context overflow and token issues

Measure speed, accuracy, and scalability, Choose the right store for project needs

Build custom prompt templates and retrievers, Modify LangChain components for full control

Summarize research and manage notes, Generate insights with personal knowledge bases

Compare prompting, RAG, and fine-tuning, Choose methods based on cost, control, or customization

Define objectives and KPIs, Prepare training/evaluation workflows

Extract/label data from public sources, Generate synthetic data with GPT

Clean and normalize text, Remove duplicates and irrelevant entries

Structure data for Hugging Face Datasets library, Version datasets with metadata

Train models like BoW + Logistic Regression, Use TF-IDF or CountVectorizer as baselines

Choose metrics: loss, accuracy, F1-score, Track business-level KPIs

Format instruction-response schemas, Ensure consistent training data

Upload dataset via CLI/API, Monitor progress and retrieve models

Visualize metrics and loss, Compare model runs

Use Low-Rank Adaptation (LoRA) for efficient training, Apply QLoRA for 4-bit quantized fine-tuning

Use double quantization (NF4), Preserve accuracy while reducing compute

Integrate adapters with transformers, Swap between base and fine-tuned weights

Business problem → dataset → model → endpoint, Deploy and monitor in production

Deploy and monitor production-grade models, Learn latency, load balancing, and rate limits

Set up Modal for inference hosting, Build Python APIs that scale automatically

Enable negotiation and task delegation, Use memory and tools across agents

Optimize Chroma for write/read performance, Partition data for parallel search

Use t-SNE, UMAP, or PCA, Analyze clusters and semantic proximity

Enforce JSON schema validation, Prevent malformed responses

Implement reflection and self-prompting, Simulate goal setting and execution

Review design choices and trade-offs, Plan iterative improvements