Teresa Torres ✓

@ttorres · Bend, OR · joined 28 Mar 2007

Author of Continuous Discovery Habits, Speaker, Coach Learn more: https://t.co/LamDBWnlR2

57 579Followers

2 293Following

23 305Posts total

17.4KViews on collected posts

Últimas publicaciones

Teresa Torres ✓@ttorres5 Sep

@ttorres Nice. I wrote about evals the other day too! 308 views · 1 likes · 0 reposts · 0 replies Open on X →

Teresa Torres ✓@ttorres4 Sep

These results are pretty surprising to me. https://t.co/oosBAqX7vh

2K views · 8 likes · 0 reposts · 5 replies Open on X →

Teresa Torres ✓@ttorres4 Sep

@ttorres Evals are the right concept, but most teams are framing it as a testing exercise, when it should be treated as a Risk Mitigation Discipline. 240 views · 1 likes · 0 reposts · 0 replies Open on X →

Teresa Torres ✓@ttorres4 Sep

@ttorres 同感。作为后端，我入门 evals 最大的门槛是术语：golden set、LLM-as-judge 全是 ML 黑话。后来发现从最土的开始就行：存 20 条线上真实 case，每次改 prompt 跑一遍人工对答案，比没有 evals 强太多。产品团队缺的是这种起点，不是 framework。 262 views · 1 likes · 0 reposts · 0 replies Open on X →

Teresa Torres ✓@ttorres3 Sep

@ttorres Evals start when you define fail, not when you pick a judge. Without that, LLM-as-judge is just another soft opinion. 71 views · 0 likes · 0 reposts · 0 replies Open on X →

Teresa Torres ✓@ttorres3 Sep

@ttorres Evals are the difference between guessing and knowing. The tricky part is designing them to test real user workflows, not just happy paths. What's your approach for capturing edge cases? 14 views · 0 likes · 0 reposts · 0 replies Open on X →

Teresa Torres ✓@ttorres2 Sep

@ttorres The framing of evals as fundamentally different from traditional QA is the part more teams need to internalize because you can't test once and call it done. 32 views · 1 likes · 0 reposts · 0 replies Open on X →

Teresa Torres ✓@ttorres2 Sep

"The only way to know if our AI products and workflows are any good is with evals." 💡 If you're using AI to write PRDs, analyze customer feedback, or build customer-facing AI products, you need to understand evals. This guide breaks down what evals actually are and why product 8.7K views · 94 likes · 8 reposts · 5 replies Open on X →

Teresa Torres ✓@ttorres2 Sep

Let's talk about evals... AI without evals is like having junior colleagues and never checking their work. Ever. Just hoping they get it right every time. Obviously not a great strategy! Running evals occasionally isn't much better. That's the equivalent of a 1:1 every few http

607 views · 7 likes · 2 reposts · 4 replies Open on X →

Teresa Torres ✓@ttorres14 Aug

How does Capital One still not have 2FA? 2.6K views · 3 likes · 0 reposts · 1 replies Open on X →

Teresa Torres ✓@ttorres5 Aug

"I'm going to tell you my story because I think it's an amazing story of continuous improvement, of how teeny-tiny steps compound over time." Over the past year, my work has transformed completely—and it all started with a broken ankle and a willingness to get curious about AI.

2.6K views · 17 likes · 2 reposts · 7 replies Open on X →

Frente a cuentas del mismo tamaño

11 publicaciones de los últimos 90 días, junto al rango de 10K–100K seguidores. por debajo de sus pares en alcance y en interacción.

Visualizaciones medianas308esta cuenta987mediana de 10K–100K

Alcance, %0.53%esta cuenta3.79%mediana de 10K–100K

Interacción, %0.42%esta cuenta1.52%mediana de 10K–100K

Métrica	Esta cuenta	Mediana de 10K–100K	Proporción
Visualizaciones medianas por publicación	308	987	0.31×
Alcance (visualizaciones ÷ seguidores)	0.53%	3.79%	0.14×
Tasa de interacción	0.42%	1.52%	0.27×

Otras cuentas de este rango → Comparar con otra cuenta → Cómo se construyen estas referencias →

Growth & engagement

How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.

Views per post

2.6K5 Aug

2.6K14 Aug

6072 Sep

8.7K

143 Sep

2624 Sep

240

3085 Sep

Last 11 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

0.99%5 Aug

0.15%14 Aug

2.31%2 Sep

1.25%

3.12%

0.00%3 Sep

0.00%

0.38%4 Sep

0.42%

0.64%

0.32%5 Sep

Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.52%.

What the audience does

Likes32.5%133 in total

Reposts2.9%12 in total

Replies5.4%22 in total

Quotes0.5%2 in total

Bookmarks58.7%240 in total

Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.

The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.

Últimas publicaciones

Frente a cuentas del mismo tamaño

Growth & engagement

Views per post

Engagement rate per post

What the audience does

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