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PromptEngineering
Author:LeeBoonstra
PromptEngineering
Acknowledgements
Contentcontributors
MichaelShermanYuanCao
ErickArmbrustAnantNawalgariaAntonioGulliSimoneCammel
CuratorsandEditors
AntonioGulliAnantNawalgariaGraceMollison
TechnicalWriter
JoeyHaymaker
Designer
MichaelLanning
February2025 2
Tableofcontents
Introduction 6
Promptengineering 7
LLMoutputconfiguration 8
Outputlength 8
Samplingcontrols 9
Temperature 9
Top-Kandtop-P 10
Puttingitalltogether 11
Promptingtechniques 13
Generalprompting/zeroshot 13
One-shot&few-shot 15
System,contextualandroleprompting 18
Systemprompting 19
Roleprompting 21
Contextualprompting 23
Step-backprompting 25
ChainofThought(CoT) 29
Self-consistency 32
TreeofThoughts(ToT) 36
ReAct(reason&act) 37
AutomaticPromptEngineering 40
Codeprompting 42
Promptsforwritingcode 42
Promptsforexplainingcode 44
Promptsfortranslatingcode 46
Promptsfordebuggingandreviewingcode 48
Whataboutmultimodalprompting? 54
BestPractices 54
Provideexamples 54
Designwithsimplicity 55
Bespecificabouttheoutput 56
UseInstructionsoverConstraints 56
Controlthemaxtokenlength 58
Usevariablesinprompts 58
Experimentwithinputformatsandwritingstyles 59
Forfew-shotpromptingwithclassificationtasks,mixuptheclasses 59
Adapttomodelupdates 60
Experimentwithoutputformats 60
JSONRepair 61
WorkingwithSchemas 62
Experimenttogetherwithotherpromptengineers 63
CoTBestpractices 64
Documentthevariouspromptattempts 64
Summary 66
Endnotes 68
PromptEngineering
February2025
PAGE
20
Youdon’tneedtobeadatascientistoramachinelearningengineer–everyonecanwriteaprompt.
Introduction
Whenthinkingaboutalargelanguagemodelinputandoutput,atextprompt(sometimesaccompaniedbyothermodalitiessuchasimageprompts)istheinputthemodeluses
topredictaspecificoutput.Youdon’tneedtobeadatascientistoramachinelearningengineer–everyonecanwriteaprompt.However,craftingthemosteffectivepromptcanbecomplicated.Manyaspectsofyourpromptaffectitsefficacy:themodelyouuse,themodel’strainingdata,themodelconfigurations,yourword-choice,styleandtone,structure,andcontextallmatter.Therefore,promptengineeringisaniterativeprocess.Inadequatepromptscanleadtoambiguous,inaccurateresponses,andcanhinderthemodel’sabilitytoprovidemeaningfuloutput.
WhenyouchatwiththeGeminichatbot,1youbasicallywriteprompts,howeverthiswhitepaperfocusesonwritingpromptsfortheGeminimodelwithinVertexAIorbyusingtheAPI,becausebypromptingthemodeldirectlyyouwillhaveaccesstotheconfigurationsuchastemperatureetc.
Thiswhitepaperdiscussespromptengineeringindetail.Wewilllookintothevariouspromptingtechniquestohelpyougettingstartedandsharetipsandbestpracticestobecomeapromptingexpert.Wewillalsodiscusssomeofthechallengesyoucanfacewhilecraftingprompts.
Promptengineering
RememberhowanLLMworks;it’sapredictionengine.Themodeltakessequentialtextasaninputandthenpredictswhatthefollowingtokenshouldbe,basedonthedataitwastrainedon.TheLLMisoperationalizedtodothisoverandoveragain,addingthepreviouslypredictedtokentotheendofthesequentialtextforpredictingthefollowingtoken.Thenext
tokenpredictionisbasedontherelationshipbetweenwhat’sintheprevioustokensandwhattheLLMhasseenduringitstraining.
Whenyouwriteaprompt,youareattemptingtosetuptheLLMtopredicttherightsequenceoftokens.Promptengineeringistheprocessofdesigninghigh-qualitypromptsthatguideLLMstoproduceaccurateoutputs.Thisprocessinvolvestinkeringtofindthebestprompt,optimizingpromptlength,andevaluatingaprompt’swritingstyleandstructureinrelation
tothetask.InthecontextofnaturallanguageprocessingandLLMs,apromptisaninputprovidedtothemodeltogeneratearesponseorprediction.
Thesepromptscanbeusedtoachievevariouskindsofunderstandingandgenerationtaskssuchastextsummarization,informationextraction,questionandanswering,textclassification,languageorcodetranslation,codegeneration,andcodedocumentationorreasoning.
PleasefeelfreetorefertoGoogle’spromptingguides2,3withsimpleandeffectivepromptingexamples.
Whenpromptengineering,youwillstartbychoosingamodel.Promptsmightneedtobeoptimizedforyourspecificmodel,regardlessofwhetheryouuseGeminilanguagemodelsinVertexAI,GPT,Claude,oranopensourcemodellikeGemmaorLLaMA.
Besidestheprompt,youwillalsoneedtotinkerwiththevariousconfigurationsofaLLM.
LLMoutputconfiguration
Onceyouchooseyourmodelyouwillneedtofigureoutthemodelconfiguration.MostLLMscomewithvariousconfigurationoptionsthatcontroltheLLM’soutput.Effectivepromptengineeringrequiressettingtheseconfigurationsoptimallyforyourtask.
Outputlength
Animportantconfigurationsettingisthenumberoftokenstogenerateinaresponse.GeneratingmoretokensrequiresmorecomputationfromtheLLM,leadingtohigherenergyconsumption,potentiallyslowerresponsetimes,andhighercosts.
ReducingtheoutputlengthoftheLLMdoesn’tcausetheLLMtobecomemorestylisticallyortextuallysuccinctintheoutputitcreates,itjustcausestheLLMtostoppredictingmoretokensoncethelimitisreached.Ifyourneedsrequireashortoutputlength,you’llalsopossiblyneedtoengineeryourprompttoaccommodate.
OutputlengthrestrictionisespeciallyimportantforsomeLLMpromptingtechniques,likeReAct,wheretheLLMwillkeepemittinguselesstokensaftertheresponseyouwant.
Beaware,generatingmoretokensrequiresmorecomputationfromtheLLM,leadingtohigherenergyconsumptionandpotentiallyslowerresponsetimes,whichleadstohighercosts.
Samplingcontrols
LLMsdonotformallypredictasingletoken.Rather,LLMspredictprobabilitiesforwhatthenexttokencouldbe,witheachtokenintheLLM’svocabularygettingaprobability.Thosetokenprobabilitiesarethensampledtodeterminewhatthenextproducedtokenwillbe.
Temperature,top-K,andtop-Parethemostcommonconfigurationsettingsthatdeterminehowpredictedtokenprobabilitiesareprocessedtochooseasingleoutputtoken.
Temperature
Temperaturecontrolsthedegreeofrandomnessintokenselection.Lowertemperaturesaregoodforpromptsthatexpectamoredeterministicresponse,whilehighertemperaturescanleadtomorediverseorunexpectedresults.Atemperatureof0(greedydecoding)is
deterministic:thehighestprobabilitytokenisalwaysselected(thoughnotethatiftwotokenshavethesamehighestpredictedprobability,dependingonhowtiebreakingisimplementedyoumaynotalwaysgetthesameoutputwithtemperature0).
Temperaturesclosetothemaxtendtocreatemorerandomoutput.Andastemperaturegetshigherandhigher,alltokensbecomeequallylikelytobethenextpredictedtoken.
TheGeminitemperaturecontrolcanbeunderstoodinasimilarwaytothesoftmaxfunctionusedinmachinelearning.Alowtemperaturesettingmirrorsalowsoftmaxtemperature(T),emphasizingasingle,preferredtemperaturewithhighcertainty.AhigherGeminitemperaturesettingislikeahighsoftmaxtemperature,makingawiderrangeoftemperaturesaround
theselectedsettingmoreacceptable.Thisincreaseduncertaintyaccommodatesscenarioswherearigid,precisetemperaturemaynotbeessentiallikeforexamplewhenexperimentingwithcreativeoutputs.
Top-Kandtop-P
Top-Kandtop-P(alsoknownasnucleussampling)4aretwosamplingsettingsusedinLLMstorestrictthepredictednexttokentocomefromtokenswiththetoppredictedprobabilities.Liketemperature,thesesamplingsettingscontroltherandomnessanddiversityofgeneratedtext.
Top-KsamplingselectsthetopKmostlikelytokensfromthemodel’spredicteddistribution.Thehighertop-K,themorecreativeandvariedthemodel’soutput;thelowertop-K,themorerestiveandfactualthemodel’soutput.Atop-Kof1isequivalenttogreedydecoding.
Top-Psamplingselectsthetoptokenswhosecumulativeprobabilitydoesnotexceedacertainvalue(P).ValuesforPrangefrom0(greedydecoding)to1(alltokensintheLLM’svocabulary).
Thebestwaytochoosebetweentop-Kandtop-Pistoexperimentwithbothmethods(orbothtogether)andseewhichoneproducestheresultsyouarelookingfor.
Puttingitalltogether
Choosingbetweentop-K,top-P,temperature,andthenumberoftokenstogenerate,dependsonthespecificapplicationanddesiredoutcome,andthesettingsallimpactoneanother.It’salsoimportanttomakesureyouunderstandhowyourchosenmodelcombinesthedifferentsamplingsettingstogether.
Iftemperature,top-K,andtop-Pareallavailable(asinVertexStudio),tokensthatmeetboththetop-Kandtop-Pcriteriaarecandidatesforthenextpredictedtoken,andthen
temperatureisappliedtosamplefromthetokensthatpassedthetop-Kandtop-Pcriteria.Ifonlytop-Kortop-Pisavailable,thebehavioristhesamebutonlytheonetop-KorPsettingisused.
Iftemperatureisnotavailable,whatevertokensmeetthetop-Kand/ortop-Pcriteriaarethenrandomlyselectedfromtoproduceasinglenextpredictedtoken.
Atextremesettingsofonesamplingconfigurationvalue,thatonesamplingsettingeithercancelsoutotherconfigurationsettingsorbecomesirrelevant.
Ifyousettemperatureto0,top-Kandtop-Pbecomeirrelevant–themostprobabletokenbecomesthenexttokenpredicted.Ifyousettemperatureextremelyhigh(above1–generallyintothe10s),temperaturebecomesirrelevantandwhatevertokensmake
itthroughthetop-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.
Asageneralstartingpoint,atemperatureof.2,top-Pof.95,andtop-Kof30willgiveyourelativelycoherentresultsthatcanbecreativebutnotexcessivelyso.Ifyouwantespeciallycreativeresults,trystartingwithatemperatureof.9,top-Pof.99,andtop-Kof40.Andifyouwantlesscreativeresults,trystartingwithatemperatureof.1,top-Pof.9,andtop-Kof20.
Finally,ifyourtaskalwayshasasinglecorrectanswer(e.g.,answeringamathproblem),startwithatemperatureof0.
NOTE:Withmorefreedom(highertemperature,top-K,top-P,andoutputtokens),theLLMmightgeneratetextthatislessrelevant.
WARNING:Haveyoueverseenaresponseendingwithalargeamountoffillerwords?Thisisalsoknownasthe"repetitionloopbug",whichisacommonissueinLargeLanguageModelswherethemodelgetsstuckinacycle,repeatedlygeneratingthesame(filler)word,phrase,orsentencestructure,oftenexacerbatedbyinappropriatetemperatureandtop-k/
top-psettings.Thiscanoccuratbothlowandhightemperaturesettings,thoughfordifferentreasons.Atlowtemperatures,themodelbecomesoverlydeterministic,stickingrigidlytothehighestprobabilitypath,whichcanleadtoaloopifthatpathrevisitspreviouslygeneratedtext.Conversely,athightemperatures,themodel'soutputbecomesexcessivelyrandom,increasingtheprobabilitythatarandomlychosenwordorphrasewill,bychance,leadbacktoapriorstate,creatingaloopduetothevastnumberofavailableoptions.Inbothcases,themodel'ssamplingprocessgets"stuck,"resultinginmonotonousandunhelpfuloutputuntiltheoutputwindowisfilled.Solvingthisoftenrequirescarefultinkeringwithtemperatureandtop-k/top-pvaluestofindtheoptimalbalancebetweendeterminismandrandomness.
Promptingtechniques
LLMsaretunedtofollowinstructionsandaretrainedonlargeamountsofdatasotheycanunderstandapromptandgenerateananswer.ButLLMsaren’tperfect;thecleareryourprompttext,thebetteritisfortheLLMtopredictthenextlikelytext.Additionally,specifictechniquesthattakeadvantageofhowLLMsaretrainedandhowLLMsworkwillhelpyougettherelevantresultsfromLLMs
Nowthatweunderstandwhatpromptengineeringisandwhatittakes,let’sdiveintosomeexamplesofthemostimportantpromptingtechniques.
Generalprompting/zeroshot
Azero-shot5promptisthesimplesttypeofprompt.ItonlyprovidesadescriptionofataskandsometextfortheLLMtogetstartedwith.Thisinputcouldbeanything:aquestion,astartofastory,orinstructions.Thenamezero-shotstandsfor’noexamples’.
Let’suseVertexAIStudio(forLanguage)inVertexAI,6whichprovidesaplaygroundtotestprompts.InTable1,youwillseeanexamplezero-shotprompttoclassifymoviereviews.
Thetableformatasusedbelowisagreatwayofdocumentingprompts.Yourpromptswilllikelygothroughmanyiterationsbeforetheyendupinacodebase,soit’simportanttokeeptrackofyourpromptengineeringworkinadisciplined,structuredway.Moreonthistableformat,theimportanceoftrackingpromptengineeringwork,andthepromptdevelopmentprocessisintheBestPracticessectionlaterinthischapter(“Documentthevariouspromptattempts”).
Themodeltemperatureshouldbesettoalownumber,sincenocreativityisneeded,andweusethegemini-prodefaulttop-Kandtop-Pvalues,whicheffectivelydisablebothsettings(see‘LLMOutputConfiguration’above).Payattentiontothegeneratedoutput.Thewordsdisturbingandmasterpieceshouldmakethepredictionalittlemorecomplicated,asbothwordsareusedinthesamesentence.
NameGoalModel
TemperatureTop-KPrompt
1_1_movie_classification
Classifymoviereviewsaspositive,neutralornegative.gemini-pro
0.1 TokenLimit 5
N/A Top-P 1
ClassifymoviereviewsasPOSITIVE,NEUTRALorNEGATIVE.Review:"Her"isadisturbingstudyrevealingthedirectionhumanityisheadedifAIisallowedtokeepevolving,unchecked.Iwishthereweremoremovieslikethismasterpiece.Sentiment:
Output POSITIVE
Table1.Anexampleofzero-shotprompting
Whenzero-shotdoesn’twork,youcanprovidedemonstrationsorexamplesintheprompt,whichleadsto“one-shot”and“few-shot”prompting.Generalprompting/zeroshot
One-shot&few-shot
WhencreatingpromptsforAImodels,itishelpfultoprovideexamples.Theseexamplescanhelpthemodelunderstandwhatyouareaskingfor.Examplesareespeciallyusefulwhenyouwanttosteerthemodeltoacertainoutputstructureorpattern.
Aone-shotprompt,providesasingleexample,hencethenameone-shot.Theideaisthemodelhasanexampleitcanimitatetobestcompletethetask.
Afew-shotprompt7providesmultipleexamplestothemodel.Thisapproachshowsthemodelapatternthatitneedstofollow.Theideaissimilartoone-shot,butmultipleexamplesofthedesiredpatternincreasesthechancethemodelfollowsthepattern.
Thenumberofexamplesyouneedforfew-shotpromptingdependsonafewfactors,includingthecomplexityofthetask,thequalityoftheexamples,andthecapabilitiesofthegenerativeAI(genAI)modelyouareusing.Asageneralruleofthumb,youshoulduseatleastthreetofiveexamplesforfew-shotprompting.However,youmayneedtousemoreexamplesformorecomplextasks,oryoumayneedtousefewerduetotheinputlengthlimitationofyourmodel.
Table2showsafew-shotpromptexample,let’susethesamegemini-promodelconfigurationsettingsasbefore,otherthanincreasingthetokenlimittoaccommodatetheneedforalongerresponse.
Goal
ParsepizzaorderstoJSON
Prompt
Parseacustomer'spizzaorderintovalidJSON:
EXAMPLE:
Iwantasmallpizzawithcheese,tomatosauce,andpepperoni.JSONResponse:
```
{
"size":"small",
"type":"normal",
"ingredients":[["cheese","tomatosauce","peperoni"]]
}
```
Continuesnextpage...
1
Top-P
N/A
Top-K
250
TokenLimit
0.1
Temperature
gemini-pro
Model
Prompt
EXAMPLE:
CanIgetalargepizzawithtomatosauce,basilandmozzarella
{
"size":"large",
"type":"normal",
"ingredients":[["tomatosauce","bazel","mozzarella"]]
}
Now,Iwouldlikealargepizza,withthefirsthalfcheeseandmozzarella.Andtheothertomatosauce,hamandpineapple.
JSONResponse:
Output
```
{
"size":"large",
"type":"half-half",
"ingredients":[["cheese","mozzarella"],["tomatosauce","ham","pineapple"]]
}
```
Table2.Anexampleoffew-shotprompting
Whenyouchooseexamplesforyourprompt,useexamplesthatarerelevanttothetaskyouwanttoperform.Theexamplesshouldbediverse,ofhighquality,andwellwritten.Onesmallmistakecanconfusethemodelandwillresultinundesiredoutput.
Ifyouaretryingtogenerateoutputthatisrobusttoavarietyofinputs,thenitisimportanttoincludeedgecasesinyourexamples.Edgecasesareinputsthatareunusualorunexpected,butthatthemodelshouldstillbeabletohandle.
System,contextualandroleprompting
System,contextualandrolepromptingarealltechniquesusedtoguidehowLLMsgeneratetext,buttheyfocusondifferentaspects:
Systempromptingsetstheoverallcontextandpurposeforthelanguagemodel.Itdefinesthe‘bigpicture’ofwhatthemodelshouldbedoing,liketranslatingalanguage,classifyingareviewetc.
Contextualpromptingprovidesspecificdetailsorbackgroundinformationrelevanttothecurrentconversationortask.Ithelpsthemodeltounderstandthenuancesofwhat’sbeingaskedandtailortheresponseaccordingly.
Rolepromptingassignsaspecificcharacteroridentityforthelanguagemodeltoadopt.Thishelpsthemodelgenerateresponsesthatareconsistentwiththeassignedroleanditsassociatedknowledgeandbehavior.
Therecanbeconsiderableoverlapbetweensystem,contextual,androleprompting.E.g.apromptthatassignsaroletothesystem,canalsohaveacontext.
However,eachtypeofpromptservesaslightlydifferentprimarypurpose:
Systemprompt:Definesthemodel’sfundamentalcapabilitiesandoverarchingpurpose.
Contextualprompt:Providesimmediate,task-specificinformationtoguidetheresponse.It’shighlyspecifictothecurrenttaskorinput,whichisdynamic.
Roleprompt:Framesthemodel’soutputstyleandvoice.Itaddsalayerofspecificityandpersonality.
Distinguishingbetweensystem,contextual,androlepromptsprovidesaframeworkfordesigningpromptswithclearintent,allowingforflexiblecombinationsandmakingiteasiertoanalyzehoweachprompttypeinfluencesthelanguagemodel’soutput.
Let’sdiveintothesethreedifferentkindsofprompts.
Systemprompting
Table3containsasystemprompt,whereIspecifyadditionalinformationonhowtoreturntheoutput.Iincreasedthetemperaturetogetahighercreativitylevel,andIspecifiedahighertokenlimit.However,becauseofmyclearinstructiononhowtoreturntheoutputthemodeldidn’treturnextratext.
Goal
Classifymoviereviewsaspositive,neutralornegative.
Output
NEGATIVE
Classifymoviereviewsaspositive,neutralornegative.Onlyreturnthelabelinuppercase.
Review:"Her"isadisturbingstudyrevealingthedirectionhumanityisheadedifAIisallowedtokeepevolving,unchecked.It'ssodisturbingIcouldn'twatchit.
Sentiment:
Prompt
0.8
Top-P
40
Top-K
5
TokenLimit
1
Temperature
gemini-pro
Model
Table3.Anexampleofsystemprompting
Systempromptscanbeusefulforgeneratingoutputthatmeetsspecificrequirements.Thename‘systemprompt’actuallystandsfor‘providinganadditionaltasktothesystem’.Forexample,youcoulduseasystemprompttogenerateacodesnippetthatiscompatiblewithaspecificprogramminglanguage,oryoucoulduseasystemprompttoreturnacertainstructure.HavealookintoTable4,whereIreturntheoutputinJSONformat.
Goal
Classifymoviereviewsaspositive,neutralornegative,returnJSON.
Output
```
{
"movie_reviews":[
{
"sentiment":"NEGATIVE","name":"Her"
}
]
}
```
Classifymoviereviewsaspositive,neutralornegative.ReturnvalidJSON:
Review:"Her"isadisturbingstudyrevealingthedirectionhumanityisheadedifAIisallowedtokeepevolving,unchecked.It'ssodisturbingIcouldn'twatchit.
Schema:
```MOVIE:
{
"sentiment":String"POSITIVE"|"NEGATIVE"|"NEUTRAL","name":String
}
MOVIEREVIEWS:
{
"movie_reviews":[MOVIE]
}
```
JSONResponse:
Prompt
0.8
Top-P
40
Top-K
1024
TokenLimit
1
Temperature
gemini-pro
Model
Table4.AnexampleofsystempromptingwithJSONformat
TherearesomebenefitsinreturningJSONobjectsfromapromptthatextractsdata.Inareal-worldapplicationIdon’tneedtomanuallycreatethisJSONformat,Icanalready
returnthedatainasortedorder(veryhandywhenworkingwithdatetimeobjects),butmostimportantly,bypromptingforaJSONformatitforcesthemodeltocreateastructureandlimithallucinations.
Systempromptscanalsobereallyusefulforsafetyandtoxicity.Tocontroltheoutput,simplyaddanadditionallinetoyourpromptlike:‘Youshouldberespectfulinyouranswer.’.
Roleprompting
RolepromptingisatechniqueinpromptengineeringthatinvolvesassigningaspecificroletothegenAImodel.Thiscanhelpthemodeltogeneratemorerelevantandinformativeoutput,asthemodelcancraftitsresponsestothespecificrolethatithasbeenassigned.
Forexample,youcouldrolepromptagenAImodeltobeabookeditor,akindergartenteacher,oramotivationalspeaker.Oncethemodelhasbeenassignedarole,youcanthengiveitpromptsthatarespecifictothatrole.Forexample,youcouldpromptateachertocreatealessonplanthattheycanthenreview.HavealookintoTable5,wherethemodeltakestheroleofatravelguide.
GoalModel
TemperatureTop-KPrompt
Actastravelguideandprovide3travelsuggestionsgemini-pro
1 TokenLimit 1024
40 Top-P 0.8
Iwantyoutoactasatravelguide.Iwillwritetoyouaboutmylocationandyouwillsuggest3placestovisitnearme.Insomecases,IwillalsogiveyouthetypeofplacesIwillvisit.
Mysuggestion:"IaminAmsterdamandIwanttovisitonlymuseums."
TravelSuggestions:
Output
Rijksmuseum:World-renownedmuseum,knownforDutchmasterpieceslikeRembrandt’s“TheNightWatch.”Don’tmisstheincrediblecollectionofpaintingsandartifactsshowcasingDutchartandhistory.
VanGoghMuseum:ImmerseyourselfinthecaptivatingworksofVincentvanGogh.Themuseumhousesthelargestcollectionofhispaintingsanddrawings,including“TheStarryNight”and“Sunflowers.”
StedelijkMuseumAmsterdam:Discovermodernandcontemporaryartfromaroundtheworld.Themuseum’scollectionincludesworksbyPicasso,Kandinsky,andRietveld,housedinastrikingmodernbuilding.
Table5.Anexampleofroleprompting
Theaboveexampleshowsanexampleoftakingtheroleofatravelagent.Whenyouwouldchangetheroletoateacherofgeography,youwouldnoticethatyouwillreceiveadifferentresponse.
DefiningaroleperspectiveforanAImodelgivesitablueprintofthetone,style,andfocusedexpertiseyou’relookingfortoimprovethequality,relevance,andeffectivenessofyouroutput.
HerearesomestylesyoucanchoosefromwhichIfindeffective:
Confrontational,Descriptive,Direct,Formal,Humorous,Influential,Informal,Inspirational,Persuasive
Let’schangeourpromptinTable6toincludeahumorousandinspirationalstyle.
Prompt
Iwantyoutoactasatravelguide.Iwillwritetoyouaboutmylocationandyouwillsuggest3placestovisitnearmeinahumorousstyle.
Mysuggestion:"IaminManhattan."TravelSuggestions:
Output
BeholdtheEmpireStateofMind:AscendtothedizzyingheightsoftheEmpireStateBuildingandbaskinthegloryofManhattan'sskyline.PreparetofeellikeKingKongatoptheBigApple,minusthegiantape-sizedbanana.
GetArtsy-FartsyatMoMA:UnleashyourinnerartaficionadoattheMuseumofModernArt(MoMA).Gazeuponmasterpiecesthatwillboggleyour
mindandmakeyouquestionwhetheryourstick-figuredrawingshaveanyartisticmerit.
Shop‘TilYouDroponFifthAvenue:IndulgeinsomeretailtherapyontheiconicFifthAvenue.Braceyourselfforstickershockasyouwindow-shop(oractuallyshop)atdesignerboutiquesthatwillmakeyourwalletcry.Buthey,you’reinManhattan,soyoumightaswellembracethefabulousness!
Table6.Anexampleofrolepromptingwithahumoroustoneandstyle
Contextualprompting
Byprovidingcontextualprompts,youcanhelpensurethatyourAIinteractionsareasseamlessandefficientaspossible.Themodelwillbeabletomorequicklyunderstandyourrequestandbeabletogeneratemoreaccurateandrelevantresponses,asyoucanseeintheexampleofTable7.
GoalModel
TemperatureTop-KPrompt
Output
Suggestarticlesforablogaboutretrogamesgemini-pro
1 TokenLimit 1024
40 Top-P 0.8
Context:Youarewritingforablogaboutretro80'sarcadevideogames.
Suggest3topicstowriteanarticleaboutwithafewlinesofdescriptionofwhatthisarticleshouldcontain.
**TheEvolutionofArcadeCabinetDesign:**
Thisarticlewouldexploretheevolutionofarcadecabinetdesigns,fromtheearlywoodandmetalcabinetsofthe1970stothesleek,neon-litdesignsofthe1980sandbeyond.
**BlastFromThePast:IconicArcadeGamesofThe80’s:**
Thisarticlewouldfeatureaglimpseintothehistory,impact,andlegacyofsomeofthemosticonicandinfluentialarcadegamesofthe1980s,highlightingtheirinnovations,popularmechanics,andenduringcharm.
**TheRiseandRetroRevivalofPixelArt:**
Thisarticlewoulddelveintotheevolutionofpixelartasadefiningvisualstyleofearlyarcadegames,tracingitsroots,exploringitstechniques,anddiscussingtheresurgenceofpixelartinmoderngamesanddigitalart.
Table7.Anexampleofcontextualprom
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