Guides

Use this playbook to execute a battle tested strategy for AI transformation, that will make your business AI native.

Explore the top alternatives to Langchain for building production-grade AI apps and agents.

Learn about common architectures, frameworks and discover best practices for building agents from AI experts.

Learn about the current rate limits and strategies like exponential backoff and caching to help you avoid them.

Learn what OpenAI's logprobs are and how can you use them for your LLM applications

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A look at AI's evolution from basic, rule-based systems to fully creative agentic workflows.

A choice dependent on specific needs, document types and business requirements.

See how GPT-5 performs across benchmarks; with a big focus on health

A practical guide to building production grade, multi agent AI systems using context engineering best practices.

A practical prompting guide to get GPT-5 to work for your use case.

How verifiable mandates are creating a secure foundation for AI-driven commerce.

A deep dive into Google's latest model performance

A deep dive and breakdown into Anthropic's latest flagship model Claude Opus 4.5

Think your APM tool has your AI covered? Think again. LLMs need their own observability playbook.

A practical guide on understanding and implementing AI automations for all industries and teams.

Exploring zero-shot & few-shot prompting: usage, application methods, and limits.

We break down when Chain-of-Thought adds value, when it doesn’t, and how to use it in today’s LLMs.

You can’t improve what you can’t see, so start tracking every decision your agent makes.

Why forcing LLMs to output structured data is a flawed paradigm, and what might come next for developers.

A practical guide to deploying agentic capabilities: what works, what doesn’t, and how to keep it reliable in prod.

Capture edge cases in production and fix them in couple of minutes without redeploying you application.

Building AI agents is 10x easier with 10,000+ tools and built-in LLM tooling support

A curated list of best practices, techniques and practical advice on how to get better at prompt engineering.

LLMs carry hidden traits in their data and we have no idea how.

You can’t have effective agents without context engineering.

A side-by-side look at Humanloop and 10 other LLM platforms.

Four core practices that enable teams to move 100x faster, without sacrificing reliability.

Build a functional chatbot using Vellum AI Workflows and Lovable with just a few prompts.

A quick guide to picking the right framework for testing your AI workflows.

A wake up call to not underestimate the unique challenges of working with LLMs.

LLMs are stepping outside the sandbox. Should you let them?

Ground truths help build confidence, but they shouldn’t block progress.

Time to see if I’ve automated myself out of a job.

Feels more natural, hallucinates less, can be persuaded—and it’s not a game-changer.

Evaluating the 'thinking' of Claude 3.7 Sonnet and other reasoning models to understand how they really reason.

Learn how DeepSeek achieved OpenAI o1-level reasoning with pure RL and solved issues through multi-stage training.

Explore the fundamentals of neural scaling laws and discover the next frontier in AI model development.

Rate limiting and downtime are common issues with LLMs — here’s how to manage it in production.

Easily test your AI workflows with Vellum—generate tons of test cases automatically and catch those tricky edge case

We’re simplifying the complex world of AI development for teams of all sizes.

I’d Pay $2,000 Out of My Own Pocket to Keep Using Cursor - The tab + context is next level.

Learn how to use guardrails, online/offline evaluation metrics for various LLM use-cases.

Learn how to prompt OpenAI o1 models, understand their limits and the opportunities ahead.

Learn how to use Vellum to convert any PDF into CSV: Examples with invoice, restaurant menu and product spec.

Understand the latest benchmarks, their limitations, and how models compare.

Learn how and when to JSON mode, structured outputs and function calling for your AI application.

Learn more about the expected GPT-5 features on improved reasoning, multimodality and accuracy on math & coding

Learn how to build an AI-powered Slackbot that can answer customer queries in real-time.

Vellum now offers VPC installations for secure AI development in your cloud, keeping data private and compliant.

Learn critical strategies to build and launch AI systems quickly and reliably.

Learn how combining knowledge graphs with vector stores can make your AI applications more accurate and reliable.

Learn how to enhance long-context prompts with corpus-in-context prompting and discover the best use-cases.

Read InvestInData's guest post on their decision to invest in Vellum.

Learn the key strategies and tools for building production-ready AI systems.

Learn to build an Agent that analyzes keywords, generates articles, and refines content to meet criteria.

How can I make my prompts better if I don't know the latest prompt engineering techniques?

Discover what are the main differences between LangChain and LlamaIndex, and when to use them.

Learn how RAG compares to fine-tuning and the impact of both model techniques on LLM performance.

Learn how to use OpenAI function calling in your AI apps to enable reliable, structured outputs.

Learn how to use Tiktoken and Vellum to programmatically count tokens before running OpenAI API requests.

Learn how to improve LLM outputs, and make your setup more reliable using prompt chaining.

Will long context replace RAG? An analysis of the pros and cons of both approaches.

Learn how to use retrieval and content generation metrics to consistently evaluate and improve your RAG system.

Learn prompt engineering tips on how to make GPT-3.5 perform as good as GPT-4.

Tips to most effectively use memory for your LLM chatbot.

Learn how to prompt Claude with these 11 prompt engineering tips.

Learn how successful companies develop reliable AI products by following a proven approach.

Learn how to build and evaluate intent handler logic in your chatbot workflow

Methods and techniques to reduce hallucinations and maintain more reliable LLMs in production.

What is LLM hallucination & the four most common hallucination types and the causes for them

Comparing the performance of Gemini Pro with zero and few shot prompting when classifying customer support tickets

Learn how to use Tree of Thought prompting to improve LLM results

Discover how recent OpenAI developments have influenced user confidence and interest in OpenAI alternatives

Step-by-step instructions for configuring OpenAI on Azure

Assistants API: Easy assistant setup with memory management - but what's under the hood?

How to use Multimodal AI models to build apps that solve new tasks and offer unique experiences for end users.

LLMs can label data at the same or better quality compared to human annotators, but ~20x faster and ~7x cheaper.

Collaborating with colleagues to test prompts yields good results but it's challenging.

Rag vs Fine-Tuning vs Prompt Engineering: Learn how to pick which one is the best option for your use-case.

Dynamically swapping LoRA weights can significantly lower costs of a fine tuned model

Why fine tuning is now relevant with open source models

Tips on how to monitor your in-production LLM traffic

Tips to experiment with your LLM related prompts

Despite high potential, LLMs are not a one-size-fits all solution. Choosing the right use case for LLMs is important

Fine-tuning can provide significant benefits in cost, quality & latency when compared to prompting