Misingi ya Tathmini ya AI
AI evaluation tests whether a system meets a defined purpose under stated conditions.
Muhtasari
It combines representative examples, explicit scoring rules, and analysis of mistakes. A successful API response or a polished demonstration does not establish that the system performs the intended task reliably.
Mambo muhimu ya kuchukua
- Set acceptance criteria before testing.
- Keep a held-out evaluation set.
- Measure content, workflow outcomes, and failure handling separately.
Dive ya kina
Write the acceptance criteria first. Specify the input, expected output, tolerable errors, response-time constraints, and conditions that should cause the system to abstain or escalate. Include a simple baseline to show whether added complexity provides a practical benefit. Build separate development and evaluation sets. Development examples support iteration; a held-out set tests choices after they are made. Repeatedly tuning on the final test set turns it into another development set. Record versions so a changed score can be traced to changed data, prompts, models, or scoring. Use metrics appropriate to the task. A classifier needs class-specific error analysis; a summarizer needs checks of factual consistency and coverage; an agent needs verification of completed actions and unintended side effects. Include difficult cases rather than only typical inputs. Review results with uncertainty and consequences in mind. A rare failure may matter more than many harmless wording differences. Repeat a stochastic task enough to understand variation, and document where the evaluation does not represent actual use. Evaluation supports a decision; it does not eliminate uncertainty.
Ufahamu wa Kiufundi
A test that checks only whether an output matches a required format can miss incorrect content. Structural validity and semantic correctness need separate measurements.
Test an invoice extractor
- Prepare an invented invoice with subtotal 80, tax 8, and total 88, plus another invoice where the total is absent.
- Score field extraction and arithmetic consistency separately. Require an explicit missing value for the second document.
- Add a case with an unrelated number near the total label to check whether the system invents a convenient answer.
The exercise defines correctness beyond merely returning well-formed JSON.
Athari za kimkakati
Maamuzi ya wazi zaidi
Inakusaidia kutenganisha madai ya wazi ya kiufundi kutoka kwa lugha ya uuzaji.
Cost and budget
Unaweza kuuliza maswali ya utekelezaji bora kabla ya kutumia pesa au wakati.
Timu na mtiririko wa kazi
Timu zenye uelewa wa pamoja hufanya maamuzi bora ya bidhaa, sera na mafunzo.
Utekelezaji wa Ulimwengu Halisi
Test an extraction system on documents with absent and conflicting fields.
Verify an agent’s final state after an action instead of trusting its success message.
Hatari & Walinzi
Timu tofauti zinaweza kutumia neno moja tofauti, kwa hivyo fafanua upeo mapema.
Vigezo vinaweza kuonekana kuwa na nguvu ilhali utendakazi wa ulimwengu halisi haufanani.
Kupuuza ubora wa data na mipango ya tathmini mara nyingi huleta matokeo tete.
Ramani ya Utekelezaji
Anza na ufafanuzi wa lugha rahisi wa matokeo unayohitaji.
Chagua kipimo kimoja cha mafanikio na hali moja ya kutofaulu kabla ya kujaribu.
Tekeleza majaribio madogo yenye data wakilishi, si seti ya onyesho iliyoboreshwa.
Hati ambapo Misingi ya Tathmini ya AI inasaidia na ambapo mbinu rahisi ni bora zaidi.
Vyanzo na kusoma zaidi
- scikit-learnModel selection and evaluation
Endelea Kuchunguza
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Mwongozo unaofuata
Tathmini ya LLM
Maswali yanayoulizwa mara kwa mara
How many test examples are enough?
There is no universal count. The required evidence depends on variability, rare failure modes, acceptable uncertainty, and the consequences of errors.