The LLMDex blog

Prompt injection, data leakage, hallucination, and the operational practices that keep AI products from blowing up in your face.

o3, o4, DeepSeek-R1, GPT-5 thinking. They're slower and 5-20x more expensive per query. When does the quality bump pay back?

A working playbook for shipping an AI code-review bot that engineers actually want. Models, prompts, latency, false-positive control, and the integration patterns that work.

Deep Research, Perplexity Pro Search, and the homebrew alternatives. What architecture works in 2026, what models to use, and the pitfalls that produce confident-sounding nonsense.

What actually works for AI customer support in 2026: triage routing, RAG over your knowledge base, escalation patterns, model picks, and the metrics that matter.

Why Google bet the Gemini line on 1M-2M token context windows when no other lab thought it mattered, and how that bet is reshaping RAG architectures across the industry.

Pricing, benchmarks, agent performance, and migration notes for OpenAI's GPT-5.5. What changed from GPT-5, what didn't, and when you should upgrade.

The two models that define the production sweet spot. We benchmarked, priced, and stress-tested both. Verdict by workload.

From $0.06 / 1K tokens on GPT-3 to $0.05 / 1M tokens on GPT-5 nano. The full pricing history, the architectural shifts behind the cuts, and what they tell us about 2026.

MMLU, HumanEval, SWE-bench, GPQA, what they actually measure, how providers game them, and how to think about benchmark numbers in 2026.

What 'AI engineer' actually means, what to test for in interviews, what to pay, and the red flags that distinguish real engineers from prompt-tinkerers.

A working engineer's read on Anthropic's research priorities, business model, and product roadmap. Why Claude wins on writing and code, and what it means for the rest of 2026.

Meta gave away frontier-quality model weights for two years straight. We unpack the strategic logic, what Llama 4 actually changed, and what's next for the open-weight ecosystem.

Mistral has been the European AI lab nobody expects to keep up, and yet it does. We unpack the technical, regulatory, and commercial reasons it remains relevant against bigger US labs.

What changes when your corpus is 1M+ documents instead of 1K. Embedding choices, retrieval strategy, infrastructure cost, and the corner cases that bite you at scale.

Yes, you can serve Llama 4 70B on one H100 at production speed. Quantization, serving stack, throughput tuning, and the operational realities.

What's actually production-ready vs research-grade across the open-weight serving, training, fine-tuning, and observability stack.

How OpenAI's GPT line evolved from a 117M-parameter research model in 2018 to GPT-5.5, pricing, parameters, capabilities, and the moments that mattered.

When self-hosting beats commercial APIs on cost, when it doesn't, and the operational realities most teams underweight.

Sub-800ms end-to-end voice agents are achievable in 2026. STT, LLM routing, TTS, latency budgets, and the architectural moves that make voice feel natural.

Every SaaS company claims to be AI-native. Most aren't. Here's how to tell, for hiring, for product strategy, for buying decisions.

OpenAI hasn't announced GPT-6 yet. Based on the patterns from GPT-3 → 4 → 5, here's what to expect: capabilities, timing, pricing, and what it means for builders.

An honest postmortem from 18 months of building a programmatic SEO site for AI tools. The architectural mistakes, the editorial misjudgments, and what we'd do differently.

Half of the SaaS layer is replaceable by an LLM with the right tools. Which half, and what the timeline looks like.

DeepSeek-V3 is 671B parameters that activate 37B per token. We unpack what that means for inference cost, training economics, and why every Western lab quietly switched architectures in 2024.

P50 fast, P99 awful. Why the standard latency-optimization advice fails for production AI products, and what actually moves the needle.

Grok's most distinctive feature isn't model quality. It's the X firehose. We unpack what that means competitively, and where xAI sits in the broader AI landscape in 2026.

Six months in, the Model Context Protocol is what every editor agent uses. Here's what's working, what isn't, and how to write your own.

Cursor, Cline, Aider, Claude Code, GitHub Copilot Agent, six months of dogfooding, side-by-side. What works, what doesn't, what's next.

What it looks like to run adversarial-prompt evaluations professionally, and why it pays well.

We instrumented a real codebase agent for a quarter. Here's what each model actually costs, and why per-token rates lie.

Inspect, OpenAI Evals, LangSmith, Ragas. Pick correctly the first time. A working engineer's comparison.

The internal process for sourcing, verifying, and de-listing benchmark numbers, and why we leave fields blank.

If you're a CTO buying an AI assistant for the company, here's the list to run before you sign. License, security, data handling, vendor stability.

2M-token context shifts the architecture. We rebuilt the pipeline. Here's what we kept, what we threw away, and what we learned.

We avoided open-weight frontier models for two years. DeepSeek-V3 ended that. A blunt evaluation of what V3 does, where it loses, and when to pick it.

Reasoning models hallucinate too. Open-weight is not always cheaper. And three more myths the AI Twitter consensus needs to retire.

A decision framework, with cost numbers, for when fine-tuning beats clever prompting in 2026.

The economics of frontier inference are weirder than they look. A primer on tokens, MoEs, and why prices keep dropping by 90% per year.

Curated prompts age fast. Here's a more durable pattern for building production-grade prompts that survive model upgrades.

BFCL, t-bench, Tau-bench. Why function-calling evals matter more than MMLU now, and how to read them.

2M tokens is enough. The frontier moved to needle-in-haystack and reasoning over haystack. Why bigger context isn't the next big thing.

Phi-4, Llama-4-8B, Qwen-2.5-7B running on a MacBook. What works, what doesn't, what's next for local inference.

We migrated. Here's what each platform got right and where they fell short for technical content. Why we ended up where we did.

A short story about how an internal model-tracking spreadsheet became a public site, and what we learned along the way.

Don't fabricate. Don't omit context. The full editorial standard behind LLMDex's data and how to apply it to your own work.