Shiriki tena — maudhui ya Julien Mutzenhardt
Faini ya €17,000 kwa JSON iliyoundwa vibaya: kosa mbaya na LLM!
Msanidi programu alisababisha ucheleweshaji mkubwa katika usindikaji wa faili muhimu kwa kampuni yake. Sababu? Hitilafu rahisi ya ujumuishaji. 
Lakini alikuwa ameeleza wazi katika kidokezo chake: "Jibu kwa JSON." Tatizo ni kwamba modeli, kwa upole mkubwa, ilijibu: "Bila shaka, hapa kuna taarifa:", kabla ya kuingiza msimbo wake.
Matokeo: kichanganuzi, ambacho kilikuwa kinatarajia kitu cha JSON mbichi, kilianguka katika mzunguko. 
Somo: Kidokezo ni maagizo tu, sio wajibu.
Ni kama kumwambia mbwa wako asiguse kuku kwenye meza, kisha uende kulala. Hakuna kitu kinacholazimisha modeli ya akili bandia kutii kwa ukamilifu muundo ulioombwa.
Suluhisho halisi: "Uwekaji Vizuwizi Uliobanwa"
Badala ya kutumaini kwamba modeli itatii maagizo yako ya maandishi, ipe "JSON Schema" (ramani kamili ya jibu linalotarajiwa, ili kufafanua kwa mfano kwamba kiasi lazima kiwe nambari).
Inafanyaje kazi chini ya kifuniko?
Badala ya kuruhusu LLM kuchagua neno linalofuata kutoka msamiati wake wote, injini ya uzalishaji huhesabu upya "tokeni" zinazoruhusiwa kwa wakati halisi. Kwa mfano, kuanza kitu cha JSON, itaruhusu tu mabano ya ufunguzi {. Modeli haina chaguo lingine. Muundo hivyo unakuwa halali kwa ujenzi, ambayo inaepuka kuzindua maelfu ya majaribio mapya yasiyo ya lazima (majaribio upya)! 
Jinsi ya kuitekeleza bila kuandika upya mnyama mkubwa sana?
Tuma tu JSON Schema yako kwa API za kawaida (kama OpenAI au Anthropic), ambazo hushughulikia kuiprosesa. Na ikiwa unahifadhi modeli yako ya AI ndani ya kompyuta yako, injini kama vLLM huishughulikia kwa urahisi sawa.
Acha kufanya chochote katika uzalishaji na linda data ya pato kutoka kwa LLM zako! 
Muhtasari wa makala hii ulizalishwa na AI.
Na wewe, umewahi kuona mfumo ukilipuka katikati ya safari kwa sababu ya jibu lisilotarajiwa kutoka kwa akili bandia?
#ArtificialIntelligence #Development #JSON #LLM #OpenAI #VLLM #Programming #Tech
Ikiwa ungependa kujadili mada na mimi au kutumia ujuzi wangu, unaweza kuwasiliana nami kupitia PM au simu ya video.
Vyanzo vya LinkedIn
- Wasifu: Pierre Jean Duvivier dit Sage (LinkedIn)
- Mwandishi aliyeshirikiwa: Julien Mutzenhardt (LinkedIn)
Chapisho 🚨 Faini ya €17,000 kwa JSON iliyoundwa vibaya: kosa mbaya na LLM! 🚨 lilionekana kwanza kwenye les plumes qui mordent.
Maswali Yanayoulizwa Mara kwa Mara
FAQs 17000 in Penalties for a Badly Formatted JSON The Fatal Mistake with LLMs
1 What is this 17000 penalty about
It refers to a realworld case where an AI system produced malformed JSON output which broke a downstream process and led to a financial penalty of 17000 Its used as a cautionary example of why output formatting matters
2 What is JSON
JSON is a simple text format for structuring data as keyvalue pairs lists and nested objects Its the standard way software exchanges data
3 What does badly formatted JSON mean
JSON that breaks the syntax rules for example missing quotes trailing commas unescaped characters unbalanced brackets or extra text beforeafter the object Parsers reject it
4 Why do LLMs produce invalid JSON
LLMs generate text probabilistically They may add explanations use markdown code fences hallucinate fields truncate output or slightly deviate from the schema especially without strict instructions or validation
5 How can a formatting error cost 17000
If a system blindly trusts LLM output invalid JSON can crash automated pipelines cause wrong transactions trigger contractSLA penalties or force manual fixes One small syntax error can cascade into real financial loss
6 Whats the most common JSON mistake LLMs make
Wrapping the JSON in markdown or adding conversational text around it Other frequent issues trailing commas single quotes and truncated output
7 How do I get an LLM to return valid JSON
Ask explicitly for JSON only no explanation provide a schema or example use low temperature and most importantly validate and retry on failure
8 What is JSON schema validation
A way to check that JSON matches an expected structure Tools like JSON Schema validators catch errors before they reach production
9 Should I trust LLM output without checking it
No Always validate LLM output programmatically Treat it as untrusted input just like usersubmitted data
10 What are structured outputs or JSON mode in LLMs
Features offered by some model providers that constrain
