Why LLM Apps Break - Analysis

Hey nerds,

This week I’m sharing the most critical tips I got form Gal, Traceloop's CTO.

But first, let’s take a second to think through the most common failure modes for LLMs:

*Percentages are rounded midpoints synthesized from multiple industry benchmarks, postmortems, and vendor reliability reports (e.g., OpenAI outage analyses, AWS Bedrock RAG guidance, Cohere and Anthropic case studies, MLOps surveys, and community incident logs). This high-level view triangulates ranges reported across sources to highlight the dominant failure modes in production.

So, what’s the takeaway?

Looking at the distribution, it makes a case for a good rule of thumb - before rolling to prod an LLM-based product, someone on your team should take some time to think through (at least!) the four key most common failure mods:
Hallucinations, Timeouts, Retrieval and Tool Calls.
This could include adding test cases, running a pre-mortem - you name it.

The technical breakdown most teams ignore

Gal shared Traceloop’s biggest takeaways from working with hundreds of AI teams
There are plenty of gems in this conversation , and some of the key questions we tackled were:

• When should you start seriously thinking about logging and observability?

• How to get started?

• What should you actually measure?

Here are some starting points to pique your curiosity:
Most teams monitor tokens and latency, but real observability requires digging into:

• Prompt-response lineage: Map every output to its exact input chain (including hidden system prompts)

• Tool execution graphs: Visualize how/when external APIs, databases, and functions are triggered

• Embedding drift: Track semantic shifts in vector spaces that silently break RAG pipelines

• Context window saturation: Identify when critical info gets pushed out of the token limit.

If you are a tech lead, solopreneur, or technical PM working with LLMs - give it a listen.

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Sayo-Nara 👋🏻

Omer