2025谷歌人工智能提示詞工程白皮書

Prompt Prompt PromptLLMoutputLLMOutputSamplingTop-Kandtop-Top?Ktop?P10PuttingitallPromptingPromptingGeneralprompting/zeroSystem,contextualandroleSystemSystemRoleContextualStep-backChainofThought思維鏈（CoT）ReAct(reason&ReAct（推理與行動）AutomaticPromptCodePromptsforwritingPromptsforexplainingPromptsfortranslatingPromptsfordebuggingandreviewing48BestProvideDesignwithBespecificabouttheUseInstructionsoverControlthemaxtokenControlthemaxtokenUsevariablesinExperimentwithinputformatsandwritingForfew-shotpromptingwithclassificationtasks,mixuptheAdapttomodelExperimentwithoutputExperimentwithoutput JSONJSONWorkingwithExperimenttogetherwithotherpromptCoTBestCoTBestDocumentthevariouspromptPromptPromptPromptPrompt20252February20252FebruaryYoudon’tneedtobeadatascientistoramachinelearningengineer–everyonecanwriteaprompt.

Youdon’tneedtobeadatascientistoramachinelearningengineer–everyonecanwriteaprompt.Whenthinkingaboutalargelanguagemodelinputandoutput,atextprompt(sometimesaccompaniedbyothermodalitiessuchasimageprompts)istheinputthemodelusestopredictaspecificoutput.Youdon’tneedtobeadatascientistoramachinelearningengineer–everyonecanwriteaprompt.However,craftingthemosteffectivepromptcanbecomplicated.Manyaspectsofyourpromptaffectitsefficacy:themodelyouuse,themodel’strainingdata,themodelconfigurations,yourword-choice,styleandtone,structure,andcontextallmatter.Therefore,promptengineeringisaniterativeprocess.Inadequatepromptscanleadtoambiguous,inaccurateresponses,andcanhinderthemodel’sabilitytoprovidemeaningfuloutput.

WhenyouchatwiththeGeminichatbot,1youbasicallywriteprompts,howeverthiswhitepaperfocusesonwritingpromptsfortheGeminimodelwithinVefiexAIorbyusingtheAPI,becausebypromptingthemodeldirectlyyouwillhaveaccesstotheconfigurationsuchastemperatureetc.Thiswhitepaperdiscussespromptengineeringindetail.Wewilllookintothevariouspromptingtechniquestohelpyougettingstafiedandsharetipsandbestpracticestobecomeapromptingexpefi.Wewillalsodiscusssomeofthechallengesyoucanfacewhilecraftingprompts.RememberhowanLLMworks;it’sapredictionengine.Themodeltakessequentialtextasaninputandthenpredictswhatthefollowingtokenshouldbe,basedonthedataitwastrainedon.TheLLMisoperationalizedtodothisoverandoveragain,addingthepreviouslypredictedtokentotheendofthesequentialtextforpredictingthefollowingtoken.Thenexttokenpredictionisbasedontherelationshipbetweenwhat’sintheprevioustokensandwhattheLLMhasseenduringitstraining.Whenyouwriteaprompt,youareattemptingtosetuptheLLMtopredicttherightsequenceoftokens.Promptengineeringistheprocessofdesigninghigh-qualitypromptsthatguideLLMstoproduceaccurateoutputs.Thisprocessinvolvestinkeringtofindthebestprompt,optimizingpromptlength,andevaluatingaprompt’swritingstyleandstructureinrelationtothetask.InthecontextofnaturallanguageprocessingandLLMs,apromptisaninputprovidedtothemodeltogeneratearesponseorprediction.

GeminiVeexAIAPIGemini練數(shù)據(jù)預(yù)測下一個標(biāo)記應(yīng)該是什么。LLM被部署來反復(fù)執(zhí)行此操作，將先前預(yù)測的標(biāo)記添加到LLM設(shè)置正確的序列標(biāo)記。提示工程是設(shè)計高質(zhì)量提示的過程，LLM生成準(zhǔn)確的輸出。這個過程涉及調(diào)整以找到最佳的提示，優(yōu)化提示長度，并評估提LLM的背景下，提示是為模型提供以生Thesepromptscanbeusedtoachievevariouskindsofunderstandingandgenerationtaskssuchastextsummarization,informationextraction,questionandanswering,textclassification,languageorcodetranslation,codegeneration,andcodedocumentationorreasoning.PleasefeelfreetorefertoGoogle’spromptingguides2,3withsimpleandeffectivepromptingexamples.Whenpromptengineering,youwillstafibychoosingamodel.Promptsmightneedtobeoptimizedforyourspecificmodel,regardlessofwhetheryouuseGeminilanguagemodelsinVefiexAI,GPT,Claude,oranopensourcemodellikeGemmaorLLaMA.Besidestheprompt,youwillalsoneedtotinkerwiththevariousconfigurationsofaLLMoutputOnceyouchooseyourmodelyouwillneedtofigureoutthemodelconfiguration.MostLLMscomewithvariousconfigurationoptionsthatcontroltheLLM’soutput.Effectivepromptengineeringrequiressettingtheseconfigurationsoptimallyforyourtask.OutputAnimpofiantconfigurationsettingisthenumberoftokenstogenerateinaresponse.GeneratingmoretokensrequiresmorecomputationfromtheLLM,leadingtohigherenergyconsumption,potentiallyslowerresponsetimes,andhighercosts.

的是VeexAI中的Gemini語言模型、GPT、Claude，還是像Gemma或LLaMA這樣的開源模LLMLLM輸出配置LLM都提供了各種配置選項，這些選項LLM的輸出。有效的提示工程需要根據(jù)您的任務(wù)最優(yōu)地設(shè)置這些配置。LLM進(jìn)行更多的計算，ReducingtheoutputlengthoftheLLMdoesn’tcausetheLLMtobecomemorestylisticallyortextuallysuccinctintheoutputitcreates,itjustcausestheLLMtostoppredictingmoretokensoncethelimitisreached.Ifyourneedsrequireashofioutputlength,you’llalsopossiblyneedtoengineeryourprompttoaccommodate.OutputlengthrestrictionisespeciallyimpofiantforsomeLLMpromptingtechniques,likeReAct,wheretheLLMwillkeepemittinguselesstokensaftertheresponseyouwant.Beaware,generatingmoretokensrequiresmorecomputationfromtheLLM,leadingtohigherenergyconsumptionandpotentiallyslowerresponsetimes,whichleadstohighercosts.SamplingLLMsdonotformallypredictasingletoken.Rather,LLMspredictprobabilitiesforwhatthenexttokencouldbe,witheachtokenintheLLM’svocabularygettingaprobability.Thosetokenprobabilitiesarethensampledtodeterminewhatthenextproducedtokenwillbe.Temperature,top-K,andtop-Parethemostcommonconfigurationsettingsthatdeterminehowpredictedtokenprobabilitiesareprocessedtochooseasingleoutputtoken.Temperaturecontrolsthedegreeofrandomnessintokenselection.Lowertemperaturesaregoodforpromptsthatexpectamoredeterministicresponse,whilehighertemperaturescanleadtomorediverseorunexpectedresults.Atemperatureof0(greedydecoding)is

LLM的輸出長度并不會使其生成的輸出在風(fēng)格或文本上變得更加簡潔，它只是導(dǎo)致LLM在達(dá)到限制后停止預(yù)測更多標(biāo)記。如果你的需求需要較短的輸出長度，你可能還需要設(shè)LLM進(jìn)行更多計算，這會導(dǎo)致更高的能耗和可能更慢的響LLMsLLMsLLMtop?Ktop?P溫 0（貪婪解碼）是deterministic:thehighestprobabilitytokenisalwaysselected(thoughnotethatiftwotokenshavethesamehighestpredictedprobability,dependingonhowtiebreakingisimplementedyoumaynotalwaysgetthesameoutputwithtemperature0).Temperaturesclosetothemaxtendtocreatemorerandomoutput.Andastemperaturegetshigherandhigher,alltokensbecomeequallylikelytobethenextpredictedtoken.TheGeminitemperaturecontrolcanbeunderstoodinasimilarwaytothesoftmaxfunctionusedinmachinelearning.Alowtemperaturesettingmirrorsalowsoftmaxtemperature(T),emphasizingasingle,preferredtemperaturewithhighcefiainty.AhigherGeminitemperaturesettingislikeahighsoftmaxtemperature,makingawiderrangeoftemperaturesaroundtheselectedsettingmoreacceptable.Thisincreaseduncefiaintyaccommodatesscenarioswherearigid,precisetemperaturemaynotbeessentiallikeforexamplewhenexperimentingwithcreativeoutputs.Top-Kandtop-P(alsoknownasnucleussampling)4aretwosamplingsettingsusedinLLMstorestrictthepredictednexttokentocomefromtokenswiththetoppredictedprobabilities.Liketemperature,thesesamplingsettingscontroltherandomnessanddiversityofgeneratedtext.Top-KsamplingselectsthetopKmostlikelytokensfromthemodel’spredicteddistribution.Thehighertop-K,themorecreativeandvariedthemodel’soutput;thelowertop-K,themorerestiveandfactualthemodel’soutput.Atop-Kof1isequivalenttogreedydecoding.

0）。Geminisomaxsomax溫度（T），GeminisomaxTop?Ktop?P（也稱為核采樣）是兩種在大型語言模型（LLM）中使用的采樣設(shè)置，用于限Top?KTop?K個標(biāo)記。Top?K越高，模型的輸出越具創(chuàng)造性和多樣性；Top?K越低，模型的輸出越保守和事實(shí)性。Top?K1Top-Psamplingselectsthetoptokenswhosecumulativeprobabilitydoesnotexceedacefiainvalue(P).ValuesforPrangefrom0(greedydecoding)to1(alltokensintheLLM’svocabulary).bothtogether)andseewhichoneproducestheresultsyouarelookingfor.PuflingitallChoosingbetweentop-K,top-P,temperature,andthenumberoftokenstogenerate,dependsonthespecificapplicationanddesiredoutcome,andthesettingsallimpactoneanother.It’salsoimpofianttomakesureyouunderstandhowyourchosenmodelcombinesthedifferentsamplingsettingstogether.Iftemperature,top-K,andtop-Pareallavailable(asinVefiexStudio),tokensthatmeetboththetop-Kandtop-Pcriteriaarecandidatesforthenextpredictedtoken,andtemperatureisappliedtosamplefromthetokensthatpassedthetop-Kandtop-Pcriteria.Ifonlytop-Kortop-Pisavailable,thebehavioristhesamebutonlytheonetop-KorPsettingisused.Iftemperatureisnotavailable,whatevertokensmeetthetop-Kand/ortop-Pcriteriaarethenrandomlyselectedfromtoproduceasinglenextpredictedtoken.Atextremesettingsofonesamplingconfigurationvalue,thatonesamplingsettingeithercancelsoutotherconfigurationsettingsorbecomesirrelevant.

Top?P（P）的前綴標(biāo)記。P0（貪婪解碼）1（LLM詞匯表中的所有標(biāo)記）。top?Ktop?P（或同時使用），看看哪一種能產(chǎn)生你想要的top?Ktop?Ptoken如果溫度、top?K和top?P都可用（如在VeexStudio中），則滿足top?K和top?P兩個條件如果溫度不可用，則滿足top?K和/或top?P條件的token將隨機(jī)選擇以生成單個下一個預(yù)測tokenIfyousettemperatureto0,top-Kandtop-Pbecomeirrelevant–themostprobabletokenbecomesthenexttokenpredicted.Ifyousettemperatureextremelyhigh(above1–generallyintothe10s),temperaturebecomesirrelevantandwhatevertokensmakeitthroughthetop-Kand/ortop-Pcriteriaarethenrandomlysampledtochooseanextpredictedtoken.Ifyousettop-Kto1,temperatureandtop-Pbecomeirrelevant.Onlyonetokenpassesthetop-Kcriteria,andthattokenisthenextpredictedtoken.Ifyousettop-Kextremelyhigh,liketothesizeoftheLLM’svocabulary,anytokenwithanonzeroprobabilityofbeingthenexttokenwillmeetthetop-Kcriteriaandnoneareselectedout.Ifyousettop-Pto0(oraverysmallvalue),mostLLMsamplingimplementationswillthenonlyconsiderthemostprobabletokentomeetthetop-Pcriteria,makingtemperatureandtop-Kirrelevant.Ifyousettop-Pto1,anytokenwithanonzeroprobabilityofbeingthenexttokenwillmeetthetop-Pcriteria,andnoneareselectedout.Asageneralstafiingpoint,atemperatureof.2,top-Pof.95,andtop-Kof30willgiveyourelativelycoherentresultsthatcanbecreativebutnotexcessivelyso.Ifyouwantespeciallywantlesscreativeresults,trystafiingwithatemperatureof.1,top-Pof.9,andtop-Kof20.Finally,ifyourtaskalwayshasasinglecorrectanswer(e.g.,answeringamathproblem),stafiwithatemperatureof0.NOTE:Withmorefreedom(highertemperature,top-K,top-P,andoutputtokens),theLLMmightgeneratetextthatislessrelevant.WARNING:Haveyoueverseenaresponseendingwithalargeamountoffillerwords?Thisisalsoknownasthe"repetitionloopbug",whichisacommonissueinLargeLanguageModelswherethemodelgetsstuckinacycle,repeatedlygeneratingthesame(filler)word,phrase,orsentencestructure,oftenexacerbatedbyinappropriatetemperatureandtop-k/

如果你將溫度設(shè)置為0，top?K和top?P就變得無關(guān)緊要了——最可能的標(biāo)記將成為下一個top?Ktop?Ptop?K1top?Ptop?K標(biāo)準(zhǔn)，top?KLLM詞匯表的top?Ktop?Ptop?Ktop?P1，任何有非零top?P0.2，top?P0.95，top?K300.9，top?P0.99，top?K400.1，top?P0.9，top?K20。最后，如果你的任務(wù)總是只有一個正確答案（例如，解答數(shù)學(xué)問題），則0注意：隨著自由度的增加（更高的溫度、top?K、top?P和輸出令牌），LLM的文字。top?ktop-psettings.Thiscanoccuratbothlowandhightemperaturesettings,thoughfordifferentreasons.Atlowtemperatures,themodelbecomesoverlydeterministic,stickingrigidlytothehighestprobabilitypath,whichcanleadtoaloopifthatpathrevisitspreviouslygeneratedtext.Conversely,athightemperatures,themodel'soutputbecomesexcessivelyrandom,increasingtheprobabilitythatarandomlychosenwordorphrasewill,bychance,leadbacktoapriorstate,creatingaloopduetothevastnumberofavailableoptions.Inbothcases,themodel'ssamplingprocessgets"stuck,"resultinginmonotonousandunhelpfuloutputuntiltheoutputwindowisfilled.Solvingthisoftenrequirescarefultinkeringwithtemperatureandtop-k/top-pvaluestofindtheoptimalbalancebetweendeterminismandrandomness.PromptingLLMsaretunedtofollowinstructionsandaretrainedonlargeamountsofdatasotheycanunderstandapromptandgenerateananswer.ButLLMsaren’tperfect;thecleareryourprompttext,thebetteritisfortheLLMtopredictthenextlikelytext.Additionally,specifictechniquesthattakeadvantageofhowLLMsaretrainedandhowLLMsworkwillhelpyougettherelevantresultsfromLLMsNowthatweunderstandwhatpromptengineeringisandwhatittakes,let’sdiveintosomeexamplesofthemostimpofiantpromptingtechniques.Generalprompting/zeroAzero-shot5promptisthesimplesttypeofprompt.ItonlyprovidesadescriptionofataskandsometextfortheLLMtogetstafiedwith.Thisinputcouldbeanything:aquestion,astafiofastory,orinstructions.Thenamezero-shotstandsfor’noexamples’.

k/top?pLLMLLMLLMLLMLLMLLM獲取相關(guān)結(jié)果。zero-shot5提示是最簡單類型的提示。它只提供任務(wù)的描述和一些文本，讓LLMLet’suseVefiexAIStudio(forLanguage)inVefiexAI,6whichprovidesaplaygroundtotestprompts.InTable1,youwillseeanexamplezero-shotprompttoclassifymoviereviews.Thetableformatasusedbelowisagreatwayofdocumentingprompts.Yourpromptswilllikelygothroughmanyiterationsbeforetheyendupinacodebase,soit’simpofianttokeeptrackofyourpromptengineeringworkinadisciplined,structuredway.Moreonthistableformat,theimpofianceoftrackingpromptengineeringwork,andthepromptdevelopmentprocessisintheBestPracticessectionlaterinthischapter(“DocumentthevariouspromptThemodeltemperatureshouldbesettoalownumber,sincenocreativityisneeded,andweusethegemini-prodefaulttop-Kandtop-Pvalues,whicheffectivelydisablebothsettings(see‘LLMOutputConfiguration’above).Payattentiontothegeneratedoutput.Thewordsdisturbingandmasterpieceshouldmakethepredictionalittlemorecomplicated,asbothwordsareusedinthesamesentence.

VeexAI（語言版）VeexAIStudio1程工作的重要性以及提示開發(fā)過程，請參閱本章后面的最佳實(shí)踐部分（）gemini?protop?Ktop?P（LLM）。請注意生成的輸disturbingmasterpiece應(yīng)使預(yù)測稍微復(fù)雜一些，因?yàn)檫@兩個單詞都出現(xiàn)在同一個 ClassifymoviereviewsasPOSITIVE,NEUTRALorNEGATIVE.Review:"Her"isadisturbingstudyrevealingthedirectionhumanityisheadedifAIisallowedtokeepevolving,unchecked.Iwishthereweremoremovieslikethismasterpiece.Top-Top-gemini-Classifymoviereviewsaspositive,neutralorClassifymoviereviewsasPOSITIVE,NEUTRALorNEGATIVE.Review:"Her"isadisturbingstudyrevealingthedirectionhumanityisheadedifAIisallowedtokeepevolvingunchecked.Iwishthereweremoremovieslikethismasterpiece.Top-Top-Tokenngemini-Table1.Anexampleofzero-shotWhenzero-shotdoesn’twork,youcanprovidedemonstrationsorexamplesintheprompt,whichleadsto“one-shot”and“few-shot”prompting.Generalprompting/zeroshotOne-shot&few-WhencreatingpromptsforAImodels,itishelpfultoprovideexamples.Theseexamplescanhelpthemodelunderstandwhatyouareaskingfor.Examplesareespeciallyusefulwhenyouwanttosteerthemodeltoacefiainoutputstructureorpattern.Aone-shotprompt,providesasingleexample,hencethenameone-shot.Theideaisthemodelhasanexampleitcanimitatetobestcompletethetask.Afew-shotprompt7providesmultipleexamplestothemodel.Thisapproachshowsthemodelapatternthatitneedstofollow.Theideaissimilartoone-shot,butmultipleexamplesofthedesiredpatternincreasesthechancethemodelfollowsthepattern.

AI單樣本提示提供了一個singlemultiple個示例給模型。這種方法向模型展示了一個它需要遵循的模式。其Thenumberofexamplesyouneedforfew-shotpromptingdependsonafewfactors,includingthecomplexityofthetask,thequalityoftheexamples,andthecapabilitiesofthegenerativeAI(genAI)modelyouareusing.Asageneralruleofthumb,youshoulduseatleastthreetofiveexamplesforfew-shotprompting.However,youmayneedtousemoreexamplesformorecomplextasks,oryoumayneedtousefewerduetotheinputlengthlimitationofyourmodel.Table2showsafew-shotpromptexample,let’susethesamegemini-promodelconfigurationsettingsasbefore,otherthanincreasingthetokenlimittoaccommodatetheneedforalongerresponse.

2gemini?pro模型配置設(shè)置，除了增token ParsepizzaParsepizzaorderstoParseacustomer'spizzaorderintovalidIwantasmallpizzawithcheese,tomatosauce,andpepperoni.JSONResponse:ContinuesnextTop-Top-gemini-Parseacustomer'spizzaorderintovalidIwantasmallpizzawithcheese,tomatosauce,andpepperoni.JSONResponse: Top-Top-Tokenngemini- CanCanIgetalargepizzawithtomatosauce,basilandNow,Iwouldlikealargepizza,withthefirsthalfcheeseandmozzarella.Andtheothertomatosauce,hamandpineapple.JSON"ham","pineapple"]]CanIgetalargepizzawithtomatosauce,basilandNow,Iwouldlikealargepizza,withthefirsthalfcheeseandmozzarella.Andtheothertomatosauce,hamandpineapple.JSON"ham","pineapple"]]Table2.Anexampleoffew-shotWhenyouchooseexamplesforyourprompt,useexamplesthatarerelevanttothetaskyouwanttoperform.Theexamplesshouldbediverse,ofhighquality,andwellwritten.Onesmallmistakecanconfusethemodelandwillresultinundesiredoutput.Ifyouaretryingtogenerateoutputthatisrobusttoavarietyofinputs,thenitisimpofianttoincludeedgecasesinyourexamples.Edgecasesareinputsthatareunusualorunexpected,butthatthemodelshouldstillbeabletohandle.

Table2.Anexampleoffew-shotSystem,contextualandroleSystem,contextualandrolepromptingarealltechniquesusedtoguidehowLLMsgeneratetext,buttheyfocusondifferentaspects:Systempromptingsetstheoverallcontextandpurposeforthelanguagemodel.Itdefinesthe‘bigpicture’ofwhatthemodelshouldbedoing,liketranslatingalanguage,classifyingareviewetc.Contextualpromptingprovidesspecificdetailsorbackgroundinformationrelevanttothecurrentconversationortask.Ithelpsthemodeltounderstandthenuancesofwhat’sbeingaskedandtailortheresponseaccordingly.Rolepromptingassignsaspecificcharacteroridentityforthelanguagemodeltoadopt.associatedknowledgeandbehavior.Therecanbeconsiderableoverlapbetweensystem,contextual,androleprompting.E.g.apromptthatassignsaroletothesystem,canalsohaveacontext.However,eachtypeofpromptservesaslightlydifferentprimarySystemprompt:Definesthemodel’sfundamentalcapabilitiesandoverarchingContextualprompt:Providesimmediate,task-specificinformationtoguidetheresponse.It’shighlyspecifictothecurrenttaskorinput,whichisdynamic.Roleprompt:Framesthemodel’soutputstyleandvoice.Itaddsalayerofspecificityandpersonality.

LLMDistinguishingbetweensystem,contextual,androlepromptsprovidesaframeworkfordesigningpromptswithclearintent,allowingforflexiblecombinationsandmakingiteasiertoanalyzehoweachprompttypeinfluencesthelanguagemodel’soutput.Let’sdiveintothesethreedifferentkindsofSystem Table3containsasystemprompt,whereIspecifyadditionalinformationonhowtoreturntheoutput.Iincreasedthetemperaturetogetahighercreativitylevel,andIspecifiedahighertokenlimit.However,becauseofmyclearinstructiononhowtoreturntheoutputthemodeldidn’treturnextratext.

ClassifymovieClassifymoviereviewsaspositive,neutralorClassifymoviereviewsaspositive,neutralornegative.Onlyreturnthelabelinuppercase.Review:"Her"isadisturbingstudyrevealingthedirectionhumanityisheadedifAIisallowedtokeepevolving,unchecked.It'ssodisturbingIcouldn'twatchit.Top-Top-gemini-Classifymoviereviewsaspositive,neutralornegative.Onlyreturnthelabelinuppercase.Review:"Her"isadisturbingstudyrevealingthedirectionhumanityisheadedifAIisallowedtokeepevolving,unchecked.It'ssodisturbingIcouldn'twatchit.nTop-Top-gemini-Table3.AnexampleofsystemSystempromptscanbeusefulforgeneratingoutputthatmeetsspecificrequirements.Thename‘systemprompt’actuallystandsfor‘providinganadditionaltasktothesystem’.Forexample,youcoulduseasystemprompttogenerateacodesnippetthatiscompatiblewithaspecificprogramminglanguage,oryoucoulduseasystemprompttoreturnacefiainstructure.HavealookintoTable4,whereIreturntheoutputinJSONformat.

4JSONgemini-GeminiTokenTop-Top-Top-Top-nClassifymoviereviewsaspositive,neutralornegative,returnClassifymoviereviewsClassifymoviereviewsaspositive,neutralornegative,returnClassifymoviereviewsaspositive,neutralornegative.ReturnvalidJSON:Review:"Her"isadisturbingstudyrevealingthedirectionhumanityisheadedifAIisallowedtokeepevolving,unchecked.It'ssodisturbingIcouldn'twatchit."name":StringMOVIEJSON"name":"Her"將電影評論分類為正面、中性或負(fù)面，返回JSONClassifymoviereviewsaspositive,neutralornegative.ReturnvalidJSON:Review:"Her"isadisturbingstudyrevealingthedirectionhumanityisheadedifAIisallowedtokeepevolving,unchecked.It'ssodisturbingIcouldn'twatchit."name":StringMOVIEJSON"name":"Her"

4JSONTherearesomebenefitsinreturningJSONobjectsfromapromptthatextractsdata.Inareal-worldapplicationIdon’tneedtomanuallycreatethisJSONformat,Icanreturnthedatainasofiedorder(veryhandywhenworkingwithdatetimeobjects),butmostimpofiantly,bypromptingforaJSONformatitforcesthemodeltocreateastructureandlimithallucinations.Systempromptscanalsobereallyusefulforsafetyandtoxicity.Tocontroltheoutput,simplyaddanadditionallinetoyourpromptlike:‘Youshouldberespectfulinyouranswer.’.RoleRolepromptingisatechniqueinpromptengineeringthatinvolvesassigningaspecificroletothegenAImodel.Thiscanhelpthemodeltogeneratemorerelevantandinformativeoutput,asthemodelcancraftitsresponsestothespecificrolethatithasbeenassigned.Forexample,youcouldrolepromptagenAImodeltobeabookeditor,akindergafienteacher,oramotivationalspeaker.Oncethemodelhasbeenassignedarole,youcanthengiveitpromptsthatarespecifictothatrole.Forexample,youcouldpromptateachertocreatealessonplanthattheycanthenreview.HavealookintoTable5,wherethemodeltakestheroleofatravelguide.

從提取數(shù)據(jù)的提示中返回JSON對象有一些好處。在實(shí)際應(yīng)用中，我無需手動創(chuàng)建這種JSONJSON系統(tǒng)提示對于安全性和毒性也非常有用。為了控制輸出，只需在提示中添加一行，例如：‘人?！?。AIAI5，其中模型扮演了旅行指南的角色。 ActasActastravelguideandprovide3travelRijksmuseum:World-renownedmuseum,knownforDutchmasterpieceslikeRembrandt’s“TheNightWatch.”Don’tmisstheincrediblecollectionofpaintingsandafiifactsshowcasingDutchafiandhistory.VanGoghMuseum:ImmerseyourselfinthecaptivatingworksofVincentvanGogh.Themuseumhousesthelargestcollectionofhispaintingsanddrawings,including“TheStarryNight”and“Sunflowers.”3.StedelijkMuseumAmsterdam:Discovermodernandcontemporaryafifromaroundtheworld.Themuseum’scollectionincludesworksbyPicasso,Kandinsky,andRietveld,housedinastrikingmodernIwantyoutoactasatravelguide.Iwillwritetoyouaboutmylocationandyouwillsuggest3placestovisitnearme.Insomecases,IwillalsogiveyouthetypeofplacesIwillvisit.Mysuggestion:"IaminAmsterdamandIwanttovisitonlymuseums."Top-Top-gemini-Iwantyoutoactasatravelguide.Iwillwritetoyouaboutmylocationandyouwillsuggest3placestovisitnearme.Insomecases,Iwillalsogiveyouthetypeofplaceson:"IaminAmsterdamandIwanttovisitonlymuseums."nTop-Top-TokenGeminiTable5.Anexampleofrole

Theaboveexampleshowsanexampleoftakingtheroleofatravelagent.Whenyouwouldchangetheroletoateacherofgeography,youwouldnoticethatyouwillreceiveadifferentresponse.DefiningaroleperspectiveforanAImodelgivesitablueprintofthetone,style,andfocusedexpefiiseyou’relookingfortoimprovethequality,relevance,andeffectivenessofyour

AIHerearesomestylesyoucanchoosefromwhichIfindConfrontational,Descriptive,Direct,Formal,Humorous,Influential,Informal,Inspirational,PersuasiveLet’schangeourpromptinTable6toincludeahumorousandinspirational

6IwantIwantyoutoactasatravelguide.Iwillwritetoyouaboutmylocationandyouwillsuggest3placestovisitnearmeinahumorousstyle.Mysuggestion:"IaminManhattan."TravelSuggestions:BeholdtheEmpireStateofMind:AscendtothedizzyingheightsoftheEmpireStateBuildingandbaskinthegloryofManhattan'sskyline.PreparetofeellikeKingKongatoptheBigApple,minusthegiantape-sizedbanana.GetAfisy-FafisyatMoMA:UnleashyourinnerafiaficionadoattheMuseumofModernAfi(MoMA).Gazeuponmasterpiecesthatwillboggleyourmindandmakeyouquestionwhetheryourstick-figuredrawingshaveanyafiisticmerit.Shop‘TilYouDroponFifthAvenue:IndulgeinsomeretailtherapyontheiconicFifthAvenue.Braceyourselfforstickershockasyouwindow-shop(oractuallyshop)atdesignerboutiquesthatwillmakeyourwalletcry.Buthey,you’reinManhattan,soyoumightaswellembracethefabulousness!Iwantyoutoactasatravelguide.Iwillwritetoyouaboutmylocationandyouwillsuggest3placestovisitnearmeinahumorousstyle.Mysuggestion:"IaminManhattan."TravelSuggestions:2MoMASy?FaSy現(xiàn)代A（MoMA）。凝視那些讓你瞠目結(jié)舌、讓你質(zhì)疑自己的簡筆畫是否...)標(biāo)志性的第五大道。準(zhǔn)備好迎接令人震驚的標(biāo)價標(biāo)簽，當(dāng)你（或者實(shí)際上）在設(shè)計師精品店櫥窗購物時（或者購物時Table6.AnexampleofrolepromptingwithahumoroustoneandContextual

Byprovidingcontextualprompts,youcanhelpensurethatyourAIinteractionsareasseamlessandefficientaspossible.Themodelwillbeabletomorequicklyunderstandyourrequestandbeabletogeneratemoreaccurateandrelevantresponses,asyoucanseeintheexampleofTable7.

AI7 SuggestafiiclesSuggestafiiclesforablogaboutretro**TheEvolutionofArcadeCabinetThisafiiclewouldexploretheevolutionofarcadecabinetdesigns,fromtheearlywoodandmetalcabinetsofthe1970stothesleek,neon-litdesignsofthe1980sandbeyond.**BlastFromThePast:IconicArcadeGamesofTheThisafiiclewouldfeatureaglimpseintothehistory,impact,andlegacyofsomeofthemosticonicandinfluentialarcadegamesofthe1980s,highlightingtheirinnovations,popularmechanics,andenduringcharm.**TheRiseandRetroRevivalofPixelThisafiiclewoulddelveintotheevolutionofpixelafiasadefiningvisualstyleofearlyarcadegames,tracingitsroots,exploringitstechniques,anddiscussingtheresurgenceofpixelafiinmoderngamesanddigitalContext:Youarewritingforablogaboutretro80'sarcadevideogames.Suggest3topicstowriteanarticleaboutwithafewlinesofdescriptionofwhatthisarticleshouldcontain.Top-Top-gemini-Context:Youarewritingforablogaboutretro80'sarcadevideogames.S