LLMs and Prompting

Learn how to send entire codebases, legal archives, or research corpora to Claude Sonnet 4.6 in one shot and get accurate answers back.

Turn unstructured documents into a structured knowledge graph you can query, using GPT-4o for triple extraction

Route queries to vector, keyword, or SQL retrieval automatically, then let the LLM judge if the context actually answers the question.

Stop hand-tuning prompts — let DSPy compile and optimize them for your specific task and metrics

Build a prompt router that automatically picks the best model for each query using vector similarity

Save costs and boost reliability by routing each prompt to the best model with automatic cascading.

Create few-shot prompts that automatically pick the best examples for each query using vector similarity.

Get structured, validated data from LLMs every time using Instructor’s patched client with Pydantic schema enforcement.

Send prompts in any language and get responses back, with automatic translation and language detection

Chain multiple LLM calls with validated JSON schemas to build reliable AI data pipelines that never break

Give your LLM chatbots real conversation memory that persists across turns without blowing up your context window

Speed up your LLM apps by running multiple tool calls at once instead of waiting for each one sequentially.

Fine-tune large language models with prefix tuning using PEFT, cutting GPU memory by 90% while matching full fine-tuning quality

Reduce LLM API costs by 40-60% with prompt caching strategies that eliminate redundant token processing across conversation turns

Speed up multi-step LLM pipelines by chaining async API calls and batching independent prompts together

Wire together tool-calling steps and validated JSON parsing to build prompt chains that never lose data between steps

Score and compare LLM outputs systematically using rubric-based evaluation with Python and structured grading criteria

Create fault-tolerant prompt chains that fall back across OpenAI, Anthropic, and open-source models seamlessly

Stop getting unpredictable LLM outputs by enforcing structured schemas with Pydantic and OpenAI

Create maintainable prompt pipelines using Jinja2 templates with variables, conditionals, and loops

Catch prompt regressions early by scoring LLM outputs with a judge model and failing CI on quality drops

Create type-safe, version-controlled prompt templates that work across OpenAI, Anthropic, and open-source models

Stop breaking your LLM app with untested prompt changes. Version prompts in YAML and run automated regression tests.

Cut irrelevant context from your RAG pipeline and get sharper LLM answers with contextual compression

Reduce hallucinations and boost accuracy by grounding your LLM prompts with retrieved documents and citations

Add self-healing retry logic to your LLM pipelines so bad JSON, failed validations, and off-topic responses get fixed automatically.

Get reliable, typed data from LLMs with Pydantic parsing, validation, and retry strategies that handle real-world edge cases.

Use constrained decoding to guarantee your LLM produces valid JSON reasoning steps every time, not just most of the time.

Combine multiple prompts into one API call to cut token overhead, lower latency, and save money on LLM inference.

Practical techniques to compress prompts and reduce token usage without sacrificing response quality in production LLM apps.

Train a smaller, faster model that learns from GPT-4 or Claude, cutting inference costs by 10-100x

Train a custom embedding model that understands your domain’s vocabulary and retrieves better results

Set up Axolotl, prepare your dataset, configure LoRA training in YAML, and merge adapters back into the base model

Keep your LLM apps working when inputs exceed context limits using practical token management patterns

Send simple queries to cheap models and complex ones to powerful models automatically with semantic routing

Practical CoT prompting patterns that measurably improve LLM reasoning on math, code, and logic tasks.

Stop LLM hallucinations by wiring up retrieval-augmented generation with LangChain and ChromaDB

Build automated LLM evaluation suites using DeepEval’s built-in and custom metrics, integrated directly into your pytest workflow.

Align your LLM with preference data using DPOTrainer – simpler and more stable than PPO

Train your own LLM adapter on a single GPU with Unsloth, LoRA, and a custom dataset

Get faster time-to-first-token by streaming from OpenAI, Anthropic, and your own FastAPI proxy with working code.

Wire up LLM-powered tool use in Python across both OpenAI and Claude, with real code for parallel and forced calls.

Stop wrestling with malformed JSON. Use GPT-5.2’s structured outputs to enforce schemas at the token level.

Set up prompt caching for Claude and GPT APIs to slash input token costs and speed up response times.

Write better system prompts that get consistent, high-quality results from any large language model