Blog
Routing, verification, and cost.
Explainers and comparisons on how LLM routing actually works, what it can and can’t save you, and where the honest limits are. Written by the team building llm11.
Academy
September 22, 2026
What Is an LLM Confidence Score? A Practical Guide
What a confidence score actually measures, why a model's stated confidence often doesn't track its accuracy, and how calibration fixes that.
- llm confidence score
- calibration
- explainer
Academy
September 22, 2026
System 1 Thinking in AI, Explained
What Daniel Kahneman's System 1 and System 2 framing actually means, and what it means when an AI vendor calls a model a System One model.
- system one router
- system 1 thinking ai
- kahneman
- explainer
Academy
September 21, 2026
The Cheapest LLM API: What 'Cheap' Actually Means
The cheapest model on a price sheet is rarely the cheapest way to run a real workload. A worked example with current pricing shows where 'cheap' diverges.
- cost
- case study
- pricing
Reviews
September 21, 2026
LiteLLM vs OpenRouter: which one do you actually need
LiteLLM is a self-hosted proxy you run yourself. OpenRouter is a hosted API in front of hundreds of models. They get compared constantly in passing and almost never head to head. Here is the actual decision.
- comparison
- infrastructure
- routing
Academy
September 21, 2026
The routing cost nobody puts on the invoice: losing your prompt cache
Provider-side prompt caching gives large discounts on repeated calls to the same model. Route that request to a different model instead and you lose the cache hit, which is a real cost that most routing savings figures simply don't count.
- caching
- cost
- routing
- honesty
Academy
September 21, 2026
TypeSafe AI Explained: What Jev Actually Checks
Jev is TypeSafe AI's System One model: typed decisions in one pass, not generated text. What it does inside a router, and what vendor claims don't cover.
- typesafe ai
- jev
- explainer
Academy
September 21, 2026
What is LLM routing, and when does it actually save money
A router sends each request to the model that can handle it for the least money. The idea is simple. The part everyone glosses over is how the router decides, and why that decision is harder than it looks.
- explainer
- routing
- cost
Reviews
September 20, 2026
7 Langfuse Alternatives Worth Considering in 2026
Langfuse is a strong default for LLM observability, but it's not the only open source or hosted option. Here are seven real alternatives and what actually distinguishes each one.
- listicle
- evaluation
- observability
Academy
September 20, 2026
LLM Evaluation Tools: The 2026 Landscape
Frameworks like DeepEval and Ragas run in your CI. Platforms like Braintrust and LangSmith add a UI and production monitoring. Here is which is which, and how teams actually combine them.
- evaluation
- tools
- landscape
Academy
September 19, 2026
AI Cost Optimization: A Practical Playbook
AI spend is climbing fast. Here are the levers that actually move an LLM bill, in the order they usually pay off, with the honest limits of each one.
- cost
- playbook
- finops
Academy
September 19, 2026
How to Prevent LLM Hallucinations in Production
A practical playbook for reducing LLM hallucinations: retrieval, schema validation, groundedness checks, resampling, cross-model verification, and human escalation, with honest tradeoffs.
- hallucination
- how-to
- production
Reviews
September 19, 2026
12 LLM Observability Tools Compared in 2026
Langfuse, Helicone, LangSmith, Braintrust, and eight more: what each LLM observability tool actually tracks, how it's deployed, and where it stops being enough.
- listicle
- observability
- monitoring
Academy
September 18, 2026
How to Evaluate a RAG Pipeline: Metrics That Matter
Retrieval metrics and generation metrics measure different failures in a RAG system. Here is how to score both, tell them apart, and build a small eval set that catches real regressions.
- rag
- evaluation
- how-to
Reviews
September 18, 2026
Portkey vs LiteLLM: Which Gateway Fits Your Stack
Portkey is a hosted gateway with guardrails, virtual keys, and caching built in. LiteLLM is the self-hosted proxy you configure yourself. Here's how to pick between them.
- comparison
- gateway
- infrastructure
Reviews
September 17, 2026
Braintrust vs Langfuse: Choosing an Eval Platform
Braintrust is a hosted, eval-first workbench built around the prompt-iteration loop. Langfuse is open source and tracing-first, with evals bolted onto observability. Here is how to actually choose.
- comparison
- evaluation
- observability
Academy
September 17, 2026
How to Evaluate LLM Output: A Practical Rubric
A concrete rubric for scoring LLM output on correctness, groundedness, completeness, format, and safety, plus how to build a golden dataset and make evaluation a repeatable process.
- evaluation
- how-to
- quality
Academy
September 16, 2026
What Are AI Guardrails? A Plain-English Guide
AI guardrails are runtime checks on what goes into and comes out of a model: input filtering, prompt injection defense, PII redaction, output validation. Here's how they actually work.
- guardrails
- explainer
- ai safety
Academy
September 16, 2026
What Is an LLM Gateway? (And Do You Need One)
An LLM gateway is a single API sitting in front of every model provider you call. Here's what it actually fixes, what it can't, and which type fits your stack.
- gateway
- explainer
- infrastructure
Academy
September 15, 2026
What Is LLM-as-a-Judge? A Practical Guide
Using a language model to score another model's output is now the default way teams evaluate LLM systems at scale. Here is how it works, where it breaks, and how to trust it.
- evaluation
- explainer
- llm as a judge
Academy
September 15, 2026
Why Do LLMs Hallucinate? The Real Causes Explained
LLM hallucination isn't a bug that a patch will fix. It's a predictable output of how these models are trained and scored. Here's the actual mechanism.
- hallucination
- explainer
- llm behavior